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ACKNOWLEDGEMENT
First of all I want to thank Allah Almighty for giving me a chance to accomplish this assignment. This assignment gives me a lot of information on creating e-book using applications that been introduced in class. Secondly I want to give my appreciation to Sir Al Bakri for his kindness gives me guidance and help me in finishing this assignment. Without him I cannot finish this assignment completely. I also want to give my appreciation to my parents for giving me morale support to finish this assignment and not forgetting my friends that also give a helping hand. Last but not least, I like to express my gratitude to the people who give me so much inspiration and morale support for encouraging me to complete this assignment.
TABLE OF CONTENT NO. 1.
TITLE
PAGE
A BIG DATA SMART LIBRARY RECOMMENDER SYSTEM FOR AN
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EDUCATIONAL INSTITUTION 2.
A STUDY ON IMPLEMENTATION OF SMART LIBRARY SYSTEMS
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USING IOT 3.
ADVANCING LIBRARY CYBERINFRASTRUCTURE FOR BIG DATA
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SHARING AND REUSE 4.
AN EMPIRICAL STUDY ON THE FACTORS INFLUENCING MOBILE LIBRARY USAGE IN IOT ERA
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5.
BIG DATA ANALYSIS OF PUBLIC LIBRARY OPERATIONS AND SERVICES BY USING THE CHERNOFF FACE METHOD
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BIG DATA APPLICATION FRAMEWORK AND ITS FEASIBILITY ANALYSIS IN LIBRARY
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7.
CLOUD COMPUTING AND VIRTUAL MACHINES IN LIS EDUCATION: OPTIONS AND RESOURCES
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8.
CLOUD COMPUTING IN DIGITAL AND UNIVERSITY LIBRARIES
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9.
“CLOUD COMPUTING” IN LIBRARY AUTOMATION: BENEFITS
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AND DRAWBACKS 10. CLOUD STORAGE FOR DIGITAL PRESERVATION:OPTIMAL USES OF AMAZON S3 AND GLACIER
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11. CLOUD COMPUTING FOR LIBRARIES: A SWOT ANALYSIS
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12. COMPUTATIONAL ISSUES IN DIGITAL LIBRARY SEARCH
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ENGINES
13. DIGITAL ERA: UTILIZE OF CLOUD COMPUTING TECHNOLOGY
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IN DIGITAL LIBRARY 14. GLOBAL VILLAGE: MOBILE ACCESS TO LIBRARY RESOURCES
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15. IAAS CLOUD COMPUTING SERVICES FOR LIBRARIES: CLOUD
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STORAGE AND VIRTUAL MACHINES 16. IMAGINING LIBRARY 4.0: CREATING A MODEL FOR FUTURE
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LIBRARIES 17. INTEGRATION OF LIBRARY SERVICES WITH INTERNET OF
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THINGS TECHNOLOGIES 18. INTERNET OF THINGS – POTENTIAL FOR LIBRARIES
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19. INTERNET OF THINGS APPLICATIONS, CHALLENGES AND
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RELATED FUTURE TECHNOLOGIES 20. LIBRARY INSTRUCTION IN A CLOUD: PERSPECTIVES FROM THE
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TRENCHES 21. LONG-TERM PRESERVATION OF BIG DATA: PROSPECTS OF
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CURRENT STORAGE TECHNOLOGIES IN DIGITAL LIBRARIES 22. OPPORTUNITIES FOR USING WIKI TECHNOLOGIES IN
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BUILDING DIGITAL LIBRARY MODELS 23. PERCEPTION OF CLOUD COMPUTING IN DEVELOPING
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COUNTRIES A CASE STUDY OF INDIAN ACADEMIC LIBRARIES 24. PROBLEMS AND CHANGES IN DIGITAL LIBRARIES IN THE AGE
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OF BIG DATA FROM THE PERSPECTIVE OF USER SERVICES 25. PROBLEMS AND PROSPECTS OF IMPLEMENTING CLOUD COMPUTING IN UNIVERSITY LIBRARIES
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26. PROMOTING INNOVATION AND APPLICATION OF INTERNET
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OF THINGS IN ACADEMIC AND RESEARCH INFORMATION ORGANIZATIONS 27. PUBLIC LIBRARIES: ROLES IN BIG DATA
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28. RISK ASSESSMENT OF DIGITAL LIBRARY INFORMATION
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SECURITY: A CASE STUDY 29. THE IMPACT OF CLOUD COMPUTING OF THE FUTURE OF
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ACADEMIC LIBRARY PRACTICES AND SERVICES 30. THE INTERNET OF THINGS AND ITS IMPACT ON THE LIBRARY
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31. THE ‘‘INTERNET OF THINGS’’: WHAT IT IS AND WHAT IT
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MEANS FOR LIBRARIES 32. THE SCIENTIFIC INFORMATION EXCHANGE GENERAL MODEL
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AT DIGITAL LIBRARY CONTEXT: INTERNET OF THINGS 33. THINGSPEAK BASED MONITORING IOT SYSTEM FOR COUNTING PEOPLE IN A LIBRARY
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A BIG DATA SMART LIBRARY RECOMMENDER SYSTEM FOR AN EDUCATIONAL INSTITUTION
Library Hi Tech A Big Data smart library recommender system for an educational institution Aleksandar Simović,
Article information:
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To cite this document: Aleksandar Simović, (2018) "A Big Data smart library recommender system for an educational institution", Library Hi Tech, Vol. 36 Issue: 3, pp.498-523, https://doi.org/10.1108/LHT-06-2017-0131 Permanent link to this document: https://doi.org/10.1108/LHT-06-2017-0131 Downloaded on: 03 April 2019, At: 17:59 (PT) References: this document contains references to 102 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 1087 times since 2018*
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A Big Data smart library recommender system for an educational institution
498 Received 30 June 2017 Revised 30 October 2017 17 January 2018 14 March 2018 Accepted 14 March 2018
Aleksandar Simović Department of E-Business, Faculty of Organizational Sciences, University of Belgrade, Belgrade, Serbia Abstract
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Purpose – With the exponential growth of the amount of data, the most sophisticated systems of traditional libraries are not able to fulfill the demands of modern business and user needs. The purpose of this paper is to present the possibility of creating a Big Data smart library as an integral and enhanced part of the educational system that will improve user service and increase motivation in the continuous learning process through content-aware recommendations. Design/methodology/approach – This paper presents an approach to the design of a Big Data system for collecting, analyzing, processing and visualizing data from different sources to a smart library specifically suitable for application in educational institutions. Findings – As an integrated recommender system of the educational institution, the practical application of Big Data smart library meets the user needs and assists in finding personalized content from several sources, resulting in economic benefits for the institution and user long-term satisfaction. Social implications – The need for continuous education alters business processes in libraries with requirements to adopt new technologies, business demands, and interactions with users. To be able to engage in a new era of business in the Big Data environment, librarians need to modernize their infrastructure for data collection, data analysis, and data visualization. Originality/value – A unique value of this paper is its perspective of the implementation of a Big Data solution for smart libraries as a part of a continuous learning process, with the aim to improve the results of library operations by integrating traditional systems with Big Data technology. The paper presents a Big Data smart library system that has the potential to create new values and data-driven decisions by incorporating multiple sources of differential data. Keywords Libraries, Data analysis, Big Data, Data storage, Education, Recommender system Paper type Technical paper
Library Hi Tech Vol. 36 No. 3, 2018 pp. 498-523 © Emerald Publishing Limited 0737-8831 DOI 10.1108/LHT-06-2017-0131
Introduction Traditionally configured systems for data storage and analysis prevent libraries from achieving competitive advantages. Over the past decade, many academic libraries have struggled to shift the value and utility of collected data (Buckland, 2017). Library users have switched to online scholar sources of information, and academic libraries have lost their monopoly over the provision of scientific information (Chambers, 2013). Library users retrieve new data necessary for learning, evaluate new theories, or discover a new addition to knowledge. Each of these functions involves determining the specific knowledge, professional literature and other learning materials that may not be available in the library (Feisel and Rosa, 2005). Identifying and analyzing data beyond the library, through campuses and external aggregations, can develop effective services and systems bringing value to the wider institution (Showers, 2015). Large amounts of available data and the implications of differential resources increase the complexity of their collection even further (Showers, 2014). Library catalogs need to carry enough information about items and users preferences to have the capacity to determine a potentially ideal result and to respond adequately to the given query (Horstmann and Brase, 2016). Nowadays, the cumulative increase in the volume of data from various sources relational and non-relational (stored in local and in the cloud environment) has given rise to the problem of providing an efficient library service to the users. According to
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Rubin (2017), educational institutions are in a period of transition in how they deliver their library services. New methods of content delivery allow e-libraries to substitute traditional face-to-face librarian recommendations. Recommendation methods need to adapt/respond to continually improving new technologies offering the possibility of new forms of delivery just like the nature and structure of educational library catalogs might well change so as to offer content of greater interest in line with the emergence of new economic models. These challenging conditions make a smart library an inescapable part of a modern library business system, which itself is an integral part of learning processes and the educational institution. The approach used in this work is based on the idea that the integration of Big Data technologies into smart library data management ecosystem solves issues such as: how large amounts of data from different sources can be collected and connected, integrated and stored, and analyzed and visualized; and how to display the content of more interest to users through a recommender system? In order to improve the process of meeting user needs in the continuous educational cycle, the proposed Big Data recommender system enables data integration from various sources (e.g. Learning Management Systems (LMS), University online bookstore, Internet of Things (IoT), data from social media networks, and traditional library) into the smart library, making the approach particularly suitable for application in educational institutions. The main contributions of this paper are: (1) Recommendation systems were made based on practical requirements as personalized e-services that have application in different domains. Existing recommender systems mainly focus on well-known approaches and they are reviewed in the next section. This paper proposes an integrated recommendation system that complements existing systems and provides a useful guide for librarians, practitioners, and researchers in developing a Big Data smart library model, and creating a new service that will improve the educational process. (2) This paper provides a framework for an efficient application of four independent data sources into the Big Data ecosystem making the smart library an integrated part of the educational continuum. (3) For each data set, it effectively identifies the specific details and requirements for an integrated recommendation with the aim of improving the results of library operations by merging the traditional library and information systems (ISs) of an educational institution with a Big Data framework. This will motivate and support researchers and practitioners to promote the popularization of this approach. (4) It particularly suggests possible further research of integrated recommendation systems in the Big Data era with the proposition of developing a smart library suitable for an educational institution. The motivation behind this study is the lack of a comprehensive survey in the field of Big Data smart library recommenders that approaches the issue from the perspective of an educational institution as a foundation of knowledge creation and dissemination in society. The paper is organized as follows. First, it provides a review of the literature related to Big Data technologies and recommender systems with a comparison of traditional systems and other research fields in diverse domains where have found application. The next section presents the Big Data smart library model with the ultimate aim being the development of an integrated recommender system suitable for an educational institution with a detailed outline of its implementation. That is followed by an evaluation of the system and its results. The next section is a discussion. Finally, in the conclusion there is discussion which includes the limitations, theoretical and practical implications of this research and outlines future work.
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Literature review Nowadays we are confronted with a lot of different types of data which is generated at a high rate. According to Gartner, Big Data is classified as one of the most important strategic technology trends in 2017 (Panetta, 2016). International Data Corporation (IDC) in its study estimated that the overall growth of the digital universe by 2020 would reach 40ZB of data (Edjlali et al., 2012). Research and scientific institutions, as well as companies in the public and private sector, generates large amounts of data on various topics (Fey et al., 2008): climate change, video surveillance of transport infrastructure, history of patients in health institutions, purchase history from e-commerce stores, social behavior through interaction on social networks, etc. (Boyd and Crawford, 2012). This is the result of more and more devices being integrated into the business processes of organizations (Cukier and Mayer-Schoenberger, 2013). Laney (2001) has defined the challenges that bring large amounts of data through the “3Vs”. Volume – the total data set size and cumulative volume beyond the capacity of existing relational database management systems (RDBMS) to process it; velocity – the speed at which new data is created which represents dynamic data-use through the interaction of participants; and variety – different formats of incompatible and inconsistent data structures, unorganized and large, carrying information of importance waiting to be analyzed and used (Buyya et al., 2016). In 2012, Gartner expanded the Big Data definition and declared that ISs need new forms of processing to allow improved decision making, gaining insights and optimizing business processes (Beyer and Laney, 2012). IBM added another V attribute to Laney’s definition – veracity. This is known as the “4Vs” (Erl et al., 2016). Zikopoulos et al. (2012) explained the reason behind IBM’s additional V and introduced the dimension of accuracy in relation to the quality of data sources in facing Big Data initiatives. According to some researchers, there are as many as “10Vs” in Big Data analytics in scientific papers (Markus, 2015). Based on the historical growth rate of the overall data generated and data flow, Cisco claims that humanity entered the ZetaByte era in 2015 (Cisco, 2017). Big Data technologies have great application in recommender systems. They have taken the place of widely applied tools in various domains of digital businesses (Philip Chen and Zhang, 2014). However, they also have great potential to be applied in library ISs and to contribute a better understanding of user needs by proposing content of interest. The goal of the recommendation system is to reduce information overload by extracting the most relevant content and information from a vast amount of data. Adomavicius and Tuzhilin (2005) presented an overview of content-based (CB), collaborative filtering (CF) and hybrid-based recommenders, describing the limitations of these approaches and discussed possible improvements. These approaches prognosticate the level of user interest or the usefulness of a particular item and rank them according to predicted values (Bernardes et al., 2014). CB is based on the assumption that the usefulness of the item will be similar to those that the user has preferred in the past, while CF predicts the usefulness of a particular item for a user based on the evaluation of that item by other system users. The combination of these algorithms creates a hybrid-personalized system of recommendations which calculates both, the user’s rating, and the function of the content (Herlocker et al., 2000; Adomavicius and Tuzhilin, 2005; Lu et al., 2015). Recommender systems have a common feature that is recognized as an important source of information to offer to users to rate items and post reviews according to their opinion (Lin, 2014). Lee et al. (2015) advocated the need for new techniques to reduce bias in movie ratings, raising questions about the reliability of ratings as an impartial quality indicator. They also found that a prior rating by an online community as crowd vs friends can have a varying impact on subsequent user’s ratings. Gao et al. (2018) investigated the influence of cultural factors on the users’ online rating behavior focusing on how cultural values affect hotel ratings. They empirically found that reviewers from countries with high power distance give lower online hotel ratings.
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Recommender systems are widely used to suggest contacts, or activities on social media platforms (Wu et al., 2014), and to improve targeted ads by the advertising industry (Buyya et al., 2016). Practitioners can develop new marketing strategies by integrating users’ current situations and future needs by offering contextually relevant socialized recommendations (Shen et al., 2013). Lu et al. (2015) presented a comprehensive analysis of recommender systems applications reviewing up-to-date application developments and clustering them into eight main categories. In accordance with the above, even from Ranganathan’s (1931) “The Five Laws of Library science”, the question of intelligent exploitation of new technologies is raised to improve user service for human needs and creating the future. Alvin Toffler in his book Future Shock (1971) set the theory that the information overload will lead to decision-making conflict. A comprehensive overview of information overload referring to too much information identified by the academic community was presented by Eppler and Mengis (2004): excessive communication overload (Meier, 1963), information overload of sensors (Lipowski, 1975), cognitive overload (Vollmann, 1991), information knowledge overload in medicine (Hunt and Newman, 1997), syndrome of information fatigue (Wurman, 2001). Also, there are studies that have confirmed and located other types of information overload within the professional service sector (Srinivasan, 2016) ranging from business consulting (Hansen and Haas, 2001) to management meetings (Grisé and Gallupe, 1999). According to Teets, the collation of information has been going on for centuries. One of the first recorded attempts was in the middle of the third century BC when Pinakes organized a library catalog, by listing author’s names in alphabetical order (Weinberger, 2007). Little seems to have changed in the meantime – information in library catalogs were still primarily organized for the physical world (Eliot and Rose, 2009). The significant innovation occurred in 2010 when the German National Library released a library catalog linking authors’ works to others in the same fields (Svensson and Jahns, 2010). In 2011, the British Library published national library data as a linked data, describing the model of Things of Interest, where the book title linked to people, events, and places (Hodder, 2013; Teets and Goldner, 2013). In 2009, Tim Berners-Lee recommended the first step which was to set up data and information on the web in a form in which machines can naturally understand or convert into an understandable form that will lead to the end result of this process – Linked Data (Bizer et al., 2009). This is precisely the issue because modern data library systems should provide insights by utilizing various techniques, such as Big Data analytics of all type and sources of data; they should enable business analytics and real-time processing whilst increasing the capacity to deliver significant data and content of interest through the recommender system. Such system provides comprehensive logistics and an analytical platform with the fully featured tools and solutions to meet the needs of the most sophisticated and demands of modern, smart library systems. Traditional and e-library recommender systems Traditional library systems of educational institutions are usually configured as RDBMS, and they are still very wide-spread. As such, they lose the ability to process large logs, text, images, audio, video, sensor records and other complex types of data that arrive at a high rate from different sources. At present, the structured database systems that store the vast majority of organizational data are unsuitable for analytical processing. Difficulties also lie in data capture, sharing, and visualization (Ahrens et al., 2011). Big Data applications and systems are built to respond to these emerging challenges. Using Big Data frameworks allows practitioners to make decisions based on evidence rather than on intuition. Traditional library systems often suffer in both, memory use and storage capacity (Bekkerman et al., 2011). Valuable data in those systems that may carry important
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information, collected and stored at a high cost is ignored and finally deleted because of limited warehouse space (Worlton, 1971). Now, when libraries are faced with the Tony Hays fourth paradigm of data-intensive science and overwhelming data sets from many different sources, it becomes more and more difficult for a library to provide enough space to store all the necessary information that is important for users, and submitted for its long-term keeping. (Hey et al., 2009; Horstmann and Brase, 2016; Bhat, 2018). Furthermore, in the traditional RDBMS of the library, the recommendation is made by the librarians based on their experience, user’s physical cards, and the relational database server logs. The suitability of such a recommendation is not knowledge-based and consumes both, a lot of time and other resources. Recommender systems are used in e-library applications to help users locate and select information and knowledge-based sources (Porcel and Herrera-Viedma, 2010). The hybrid-based recommender system Fab, part of the Stanford University Digital Library Project, which combines CB and CF recommendation algorithms, was presented by Balabanović and Shoham (1997). Mooney and Roy (2000) developed a book recommender system with a machine-learning algorithm for text categorization. A Naive Bayesian text classifier utilized information extraction to build features of books and user preferences to find the best matched books for the observed user. Later, a personalized e-library service called CYCLADES was proposed by Renda and Straccia (2005). CYCLADES provides recommendation algorithms which rely on personalized information of the organization and users’ opinions in an integrated environment. Based on the research of Porcel et al. (2009), who developed a hybrid-based recommender to advise research resources in University Digital Libraries (UDL) to handle flexible information by means of linguistic labels by creation users’ preferences relation, Serrano-Guerrero et al. (2011) presented a recommender engine which can incorporate GoogleWave technology in UDL. All of the discussed e-library recommender systems are mostly using hybrid-based recommender approaches which combine CB and CF techniques (Lu et al., 2015). E-learning and Big Data recommender systems Recommender systems have found applications in diverse domains such as e-learning and Big Data science. Since the early 2000s, e-learning recommender systems have been increasingly popular. Based on the development of traditional e-learning systems and more than fifteen years of research studies on this topic, many practical and applicable solutions of e-learning recommenders have been developed. This type of system aims to help students choose courses, and find learning materials that they are interested in. Zaíane (2002) proposed building a software agent that uses data mining techniques, based on association rule algorithm for constructing a model which represents the behavior of a user. A personalized e-learning material recommender system was proposed by Lu (2004). When a student’s registration is obtained in the database, the system uses a computational model to identify user learning preferences which it combines with matching rules to generate a recommendation. Chen and Chao (2008) developed a system that augments traditional books with online discussion forums and learning communities. Based on their preferences, members receive messages from a web-based learning community which includes links to additional online resources. Romero et al. (2009) proposed an e-learning recommender system that utilizes Web usage mining to recommend links in a Web-based educational system. Further, a hybrid-based e-learning recommendation approach was developed by Capuano et al. (2014). This system prototype recommends learning goals generating recommendations through learning experiences and user needs. A hybrid-based approach with a three-step recommendation: mapping, utility estimation, and higher level learning goals. Extending web-based educational systems with personalized support through user centered designed recommendations was proposed by Santos et al. (2014). This study shows
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that the building of a personalized e-learning system is a process that needs to address students’ needs throughout the e-learning life cycle. Recently, De Meo et al. (2017) formed e-learning classes by evaluating trust and skills of learners and proposed a model aimed at managing the formation and the evolution of e-learning classes based on the information available on online social networks. Some of the relevant research studies that have linked Big Data recommenders with data analytics in other areas include (Khan et al., 2017): analytics in business (Duan and Xiong, 2015), climate changes (Lu et al., 2011), analytics of bank customers (Sun et al., 2014), smart cities (Khan et al., 2015), social media analytics (Burnap et al., 2015), healthcare analytics (Raghupathi and Raghupathi, 2014), railway management system analytics (Thaduri et al., 2015) and intelligent transportation (Chandio et al., 2015). Also, Saboo et al. (2016) proposed a time-varying effects model for handling the complexities associated with Big Data analytics for resources (re)allocation in marketing strategies. He et al. (2014) presented a social recommender system based on Hadoop (SRSH) to generate recommendations of similar users and user communities for finding friends and content. Ismail and Al-Feel (2015) proposed a Hadoop-based recommendation system for research papers. In the same year, Yao et al. (2015) developed a Big Data-based Hadoop ecosystem for facilitating data processing in healthcare services and clinical research. Wang (2016) designed and implemented a network recommendation system based on the Hadoop platform. Yi et al. (2017) presented a library recommendation method based on association rules combined with an artificial bee colony algorithm with the aim of producing personalized booklists using historical borrowing records. A multimedia recommender system for online social networks named SOS was presented by Amato et al. (2017). Integration NoSQL and relational database into the Hadoop ecosystem was the project of Rodrigues et al. (2018) with the aim to implement an e-commerce prototype system to manage credit card transactions, involving large volumes of data by using different technologies. The structure of each of these works is different. It is important to understand that one of the lesser-explored applications of Big Data analytics lies in the smart library recommenders of an educational institution. Moreover, the use of this synergistic approach for developing a Dig Data smart library platform for the implementation of diverse data sets and sources needs to be explored. The need for research in this field can be summarized as a lack of a comprehensive survey of the use of huge amounts of data from differential sources when creating smart library applications in a learning continuum that can benefit both, educational institution and users’ needs. Big Data smart library model This paper presents a Big Data smart library model with the aim of building a system that can recommend personalized content to users with increased precision by analyzing users interests collected from multiple sources, as well as the characteristics of content from different types of data. This process enhances the quality of the recommendation scenario as treats the relationship between the libraries and education as inseparable from one another. It can be said that there are three key strands which make this system critical for information companies which manipulate large amounts of data such as libraries. They are: switching to a scalable and elastic infrastructure, the complexity and diversity of available data, the power and value of combining different types of data. The proposed model shown in Figure 1 contains the main components necessary for the creation of a smart library. As shown, data is collected into the smart library from multiple data sources including the LMS (Despotović-Zrakić et al., 2012), the educational institution IS, social media networks (Hargittai, 2007), online bookstore server logs (Menascé et al., 1999), and the IoT (Barnaghi et al., 2012). The primary focus of the smart library shown in
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Figure 1 is the integration of multiple sources of different data and providing personalized recommendations to the user. The goal of the new Big Data smart library is the realization of a more precise and efficient recommender system. The interaction between different data sources in generating personalized recommendations is presented in an innovative way with the accessible system throughout data integration which achieves the most interoperability. The smart library connects and combines data from the following sources: information that students exchange between themselves and teachers via the news forum on the Moodle platform, including selected student courses during the learning cycle; the data set from the IS of the educational institution; information collected from social media networks; the server log files from the University online bookstore that contains personalized customer information; information collected from IoT sensors, i.e., location of the printed edition of a book in the library and its usage. This data is gathered, processed and analyzed in the Big Data ecosystem which has been selected for the realization and visualization of the final content – personalized content-aware recommendations to the user based on his/her interests. Figure 2 is a flowchart of the realized Big Data ecosystem for the smart library.
Educational institution IS
LMS Moodle
Figure 1. Smart library block diagram
Online bookstore
Social media networks
Smart library
Internet of Things (loT)
Data Integration Data Interaction Recommender system
Data sets
User
Educational institution
Online bookstore
Library
Selected courses of students
User attributes
Borrowed books
Downloadable e-books via student accounts
Offline Library
To reserve
To buy
Input
Input
User personalized preferences
Input
Online LMS Moodle
Input
Recommendation
Figure 2. Flowchart of the realized ecosystem for the smart library
Output
Master node
Slave node
Name node
Data node
Secondary name node
Task tracker
Job tracker
HDFS layer and MapReduce layer
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Data sets from various sources are loaded and integrated into the HCatalog. These different data sets include: borrowed books from the University library; downloadable e-books via student accounts through the IS of the educational institution; server logs from the university online bookstore that contain information about the users’ personal preferences; and the student’s preferences in the current school year on the LMS platform. With every new access of a registered user on the LMS platform, the Hadoop ecosystem analyzes the following: information about books attributes from the user’s logged book usage in the library; data from the IS of the educational institution about the selected courses of the student in the current school year; the purchase history and user attributes from the University online bookstore logs; performs CF on multiple data; and at the completion of the processing of given data, the user receives a recommendation of greater interest and precision with two available choices: (1) Choice 1: to reserve the recommended books in the library of the educational institution in which case the librarian performs a reservation for the user under his ID for a specified time period for borrowing. (2) Choice 2: to buy the recommended books from the University online bookstore. Data sets description A data set of the educational institution for three years of undergraduate studies contains 160 courses, seven study programs and ~2,250 students from which approximately available books to download from a student service per student in the current school year is 5-15, depending on selected subjects and available literature, with the overall of over 10 million records. The library data set contains 470,571 users and 3,955 book titles. The University online bookstore logfile that was used contains ~3,900 evaluations, ~450 users and ~1,495 items that were taken for the calculation. For these data sets and their analysis, an important factor is coverage (Sarwar et al., 2000). Coverage is a percentage of the total number of items for which the system will generate recommendations. The basic measurement of coverage is the percentage of available items. It is the percentage of all users of the system for which the forecast is requested. The common characteristics of systems that can reduce coverage are small dimensions of similarity of users and their sampling. Logfile has a high coverage of approximately 95 percent. Maximum coverage is not provided for the following reasons: there are certain items without evaluation (purchased or rated); small number of users who have evaluated a particular item; and low similarity of a particular user in the system. Each student is required to apply to a course on the LMS Moodle platform for the subjects he has chosen in the current school year, which may be between 10 and 12 courses. The LMS Moodle platform contains the following information: three years of undergraduate studies (seven study programs), one year of specialist studies (six study programs) and two years of master studies (three study programs) – which is a total of 440 courses, ~3,750 students, ~900 teachers, and over 1,500 records per day. Implementation and results For the pilot implementation, the chosen platform was open-source Apache Hadoop Hortonworks. Apache Hadoop is a widely adopted and one of the most well-established Big Data software platforms that support distributed data-intensive application and the MapReduce computational paradigm that allows parallel processing of a huge volume of heterogeneous data. MapReduce and Hadoop are considered to be the most effective and efficient framework for Big Data management (Khan et al., 2017).
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In the implemented ecosystem, data are stored in HDFS, which provides scalable and fault-tolerant storage. HDFS detects and compensates cluster errors, splitting incoming files into blocks, and storing them redundantly over clusters. The files are divided into the blocks (64MB or 128MB each), and each block is copied on more than one node. A projected system with replica possibilities allows fault tolerance – where the loss of one node will not destroy the file, and performance – where any block can be read from one or more nodes while improving the data flow. HDFS in this model of the library ecosystem provides data availability by continuously monitoring the nodes in the cluster and the blocks they manage. The individual blocks are subject to checks and controls. When a block has been read, correctness is determined (whether the recorded value is correct). If the block is damaged, it has been replaced with one of its replicas from another cluster clone (Olson, 2010; White, 2012). Parallel data processing is executed by the MapReduce programming model. MapReduce provides large data sets to be shared in clusters for parallel processing. The master node assigns tasks to the slave node and then collects the results. The model thus defined has two main steps: map – the job distribution and reduce – collecting results (Dean and Ghemawat, 2008). The Hadoop technology stack with modules of importance for the creation of a Big Data smart library is shown in Figure 3. MapReduce programming in the proposed library ecosystem is performed by the Hive module that allows for the execution of queries over large data sets and provides a mechanism for data structure projecting. The prominent feature of this layer is a structure subjected to parallelization which enables management of the large data sets in the ecosystem (Thusoo et al., 2009; George, 2011). The specified data sets were loaded into the HCatalog, ready for further processing on the management layer of
Big data Structured data
Sqoop
Unstructured data
LogFiles
Flume
Chuckwa
Data integration Pig
Hive
HADOOP MapReduce programming
HADOOP distributed file system – HDFS
Figure 3. Hadoop technology stack for the proposed smart library model
Data storage and data library
Data interaction and data visualization
HBase
HCatalog
Data intelligence and data serialization Mahout
Spark
Drill
Lucene
Avro
Zookeeper
Ambari
Ozzie
Monitoring
Management
Orchestration
the ecosystem. The data is later distributed and transferred to the HBase module which enables searching, downloading and analyzing. The query performed on the library data (shown below) aims to display users who have borrowed specific books in a given time period in 2017, and to prepare the data set for further processing (Note: The query was translated into English for easier understanding. At the Ambari module in Figure 4, the query is shown in its original form): SELECT debit.person_id,book.title,
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COUNT(debit.person_id) OVER (PARTITION BY debit.person_id ORDER BY debit.person_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
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FROM debit JOIN book ON (debit.book_id ¼ book.row) WHERE debit.taking_date W “2017-01-01” AND debit.return_date o“2017-06-14”.
The search and selection of the most appropriate items were performed according to the following six steps: (1) Step 1: the results of the executed query show book titles and the ID of a user who has borrowed the most books in the specified date range (shown in Table I). (2) Step 2: monitoring and administration in the ecosystem are executed by the Ambari module which enables installation, management, and monitoring of the Hadoop services in the cluster. The integrated module: coordinates distributed applications, synchronizes and centralizes services in the cluster, and coordinates and monitors the workflow of mutually independent Hadoop jobs (White, 2012). The result of the executed query from Ambari module is shown in Figure 4.
Figure 4. Ambari – results of the executed query
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(3) Step 3: in the implemented ecosystem, at the external and internal sources (the IS of the educational institution, the University library, the University online bookstore, and the LMS Moodle platform) the user ID is linked to the e-mail account. (4) Step 4: on the online student services of the IS of the educational institution, students could, by using their accounts, download e-books on the basis of selected subjects for the current and previous school years. The available e-books for download for the current school year per student could be in the range from 5 to 15.
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(5) Step 5: the recommender system which is integrated into the University online bookstore uses item-based CF (Linden et al., 2003). At any new access by the user to the online bookstore, the system analyzes the users’ purchase history, attributes, and personalized preferences to generate a list of recommendations and content of interest. The item-based CF compares each purchase made by users, ranks the items by similarity, and by combining the similar products generates a recommendation (Simović, 2014). To every user who has purchased or rated items from the University online bookstore, the system recommends three books. (6) Step 6: at a user’s new logon the LMS Moodle platform, the proposed Big Data recommendation system processes the data from the University library, the IS of the educational institution, the online bookstore server logs, and the LMS platform to generate a recommendation. The application is developed using the Moodle API. The algorithm calculates similarity to determine which items are most suitable for the user based on all the items in the four separate systems. The way in which the algorithm calculates the similarity of items with all others in HCatalog is shown in the following pseudo-code. HCatalog ¼ union (ProductCatalog1, ProductCatalog2, ProductCatalog3, ProductCatalog4 […])
Where the ProductCatalog1 is the library data; the ProductCatalog2 is the IS data of the educational institution; the ProductCatalog3 is the University online bookstore server logs; the ProductCatalog4 is the LMS data. Where the Selected Item is: Item borrowed from the library; or available for download from the IS of the educational institution; or purchased Item from the University online bookstore; or the student preferences in the current school year: For each Item I1 in HCatalog For each User U who Selected Item I1 For each Item I2 Selected by User U Record that a user Selected Item I1 and Item I2 For each Item I2 Compute the similarity between Item I1 and Item I2
Figure 5 shows a recommendation to a user with the same ID that the Hadoop ecosystem generates on the LMS Moodle platform using four independent sources and four integrated User ID Book titles (books borrowed from the library) Table I. User ID and book titles
376583 376583 376583 376583
Психолошки услови трансфера учења (psychological conditions of learning transfer) Fotonaponska postrojenja: planiranje (photovoltaic plants: planning) Solarne tehnologije: toplotni i fotoelektrični sistemi (solar technologies: thermal and photoelectric systems) Физика: механика чврстих (physics: solid mechanics)
Big Data
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Figure 5. Hadoop recommender on the LMS moodle platform
data sets. The user receives the following choices: to reserve the recommended books in the library of the educational institution; or to buy the recommended books from the University online bookstore. The three book titles that the Big Data ecosystem generates for the user with the same ID are shown in Table II. Evaluation of the system The evaluation of the system was conducted in two ways. For the first evaluation, Big Data analysis of the system was used to determine the most recommended books in the Hadoop ecosystem in order to discover whether those books were borrowed more often from the University library compared to the same period of the previous year. For the second evaluation of the system, an online questionnaire has been chosen as an activity for the evaluation of the implemented prototype ecosystem. The questionnaire was used to determine whether the Hadoop-based recommendation with multiple sources is more appropriate for the users (based on their impressions) than the University online bookstore recommendation system. Big Data analysis of the system In the first evaluation, Big Data analysis was used to determine the following: which books were recommended to users most often during the evaluation of the system prototype; and how many times were the recommended books borrowed from the University library in 2016 in comparison to 2017. The aim of the analysis was to evaluate the potential of the Hadoop-based recommendation ecosystem to contribute an improvement of the business performance of the organization and to an increase of the users’ usage and trust in the system. User ID
Book titles (recommended on LMS Moodle based on Hadoop ecosystem)
376583 376583 376583
Увод у проналажење информација на вебу (introduction to finding information on the web) Одрживи развој (sustainable development) Електронско банкарство (electronic banking)
Table II. LMS Moodle recommendation based on Hadoop
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The following query shows which books were most often recommended to users on the LMS Moodle platform based on the Hadoop ecosystem: SELECT recommender.book_id,book.name, COUNT(recommender.book_id) OVER (PARTITION BY recommender.person_id ORDER BY recommender.book_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING)
510
FROM recommender JOIN book ON (recommender.book_id ¼ book.count).
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The results of the executed query from the Ambari module are shown in Figure A1 and Table III. Further, the two queries were executed over two separate time periods (2016 and 2017), which were then compared. The first query determines how often recommended books were borrowed from the University library in a given time period for 2016 before the prototype of the Big Data ecosystem for the smart library was activated: SELECT recommender.book_id, book.name, COUNT(recommender.book_id) OVER (PARTITION BY recommender.book_id ORDER BY recommender.book_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) FROM recommender JOIN book ON (recommender.book_id ¼ book.count) WHERE recommender.book_id IN (“1,016”, “844”, “1,002”, “138”, “1,463”, “1,497”, “103”, “118”, “571”, “1,916”, “2,113”, “2,275”, “7”, “264”) AND recommender.date_taken W“2016-1-1” AND recommender.date_return o “2017-1-1”.
The results of the executed query are shown in Figure A2. The second query determines how many times the books were borrowed from the University library in a given time period in 2017 after the activation of the prototype of the Big Data ecosystem for the smart library: SELECT recommender.book_id, book.name, COUNT(recommender.book_id) OVER (PARTITION BY recommender.book_id ORDER BY recommender.book_id ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) ID of the books Book titles 1016 844 1002 138
Table III. The most recommended books to users
1463 1497 103 118 571 1916 2113 2275 7 264
Osnovi elektronike i telekomunikacija (Basics of electronics and telecommunications) Хидраулика: увод са примерима управљања (Hydraulics: introduction to control, examples) Моторна возила I: општи и теоријски деo (Motor vehicles I: general and theoretical part) Електричне машине: за трећи разред електротехничке школе (Electrical machines: for the third grade school of electronics) Кomutatorni motori (Commutate motors) Termoelektrane (Thermal power plants) Физика: механика чврстих (Physics: solid mechanics) Трансформатори (Transformers) Психолошки услови трансфера учења (Psychological conditions of learning transfer) Energetski transformatori i generatori (Energy transformers and generators) Фотонапонска постројења: планирање (Photovoltaic plants: planning) Solarne tehnologije: toplotni i fotoelektrični sistemi (Solar technologies: thermal and photoelectric systems) Основи рачунарске технике (Basics of computer technology) Математички приручник (Mathematics manual)
Number of times 3 2 2 3 3 3 6 6 6 6 6 6 2 2
Big Data
FROM recommender JOIN book ON (recommender.book_id ¼ book.count) WHERE recommender.book_id IN (“1,016”, “844”, “1,002”, “138”, “1,463”, “1,497”, “103”, “118”, “571”, “1,916”, “2,113”, “2,275”, “7”, “264”) AND recommender.date_taken W “2017-1-1” AND recommender.date_return o“2017-12-1”;
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Student’s impressions The second evaluation of the system consisted of an online questionnaire as the method for the evaluation of the implemented prototype ecosystem. The questionnaire was integrated into the LMS Moodle at the School of Electrical and Computer Engineering of Applied Studies, University of Belgrade and used to determine whether the Hadoop-based Recommended books (IDs)
Book titles (the most recommended books to users Borrowing (2016) Borrowing (2017) on Hadoop ecosystem) (number of times) (number of times)
1016 844 1002 138
Osnovi elektronike i telekomunikacija Хидраулика: увод са примерима управљања Моторна возила I: општи и теоријски део Електричне машине: за трећи разред електротехничке школе Кomutatorni motori Termoelektrane Физика: механика чврстих Трансформатори Психолошки услови трансфера учења Energetski transformatori i generatori Фотонапонска постројења: планирање Solarne tehnologije: toplotni i fotoelektrični sistemi Основи рачунарске технике Математички приручник
1463 1497 103 118 571 1916 2113 2275 7 264
1 1 1 1
4 4 1 1
1 4 1 1 1 1 1 4 4 1
1 4 1 4 1 25 9 2 4 1
Table IV. Comparative overview
30 2016
2017
25 20 15 10 5
7
4 26
3
75 22
16
1
8
3
Recommended books (IDs)
21 1
19
57
11
10
14 97
14 63
13 8
4
10 02
84
16
0 10
How many times the books were borrowed from the library
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The results of the executed query are shown in Figure A3. Comparative overview of recommended books borrowed in 2016 and 2017 is shown in Table IV. As shown in Figure 6, in 2016, the books that were taken for the evaluation of the system were borrowed 23 times, compared to 2017 when the same books were borrowed 62 times which is approximately 269.5 percent higher.
Figure 6. Number of times that the recommended books have been borrowed
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recommendation with multiple sources is more appropriate for the users (based on their impressions) than the University online bookstore recommendation system. The sample was 220 of students in the third year of undergraduate studies in the area of E-business and ISs during the winter semester of the 2016/17 school year. All students agreed to take part in the research. The questionnaire included ten questions. Each question was supported by Moodle: answers from multiple choices and true-false answers. Two important questions were based on the five-point Likert scale. The online questionnaire is shown in Table V.
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Analysis of results The first consideration is a Big Data analysis that illustrates the potential benefits of the application of a Big Data smart library with the integration of multiple sources of differential data in the context of meeting user needs and improving the economic benefits for the organization with the overall satisfaction of its services, through IT project management thereby promoting mutual understanding and the development of long-term relationships (Liu et al., 2017). That is confirmed by the results which are showed an increase of approximately 269.5 percent in the number of books that were borrowed from the University library from the most recommended ones on the LMS Moodle platform based on Hadoop during the prototype system testing. The second considerations dealt with the registration and purchases from the University online bookstore. The results showed that 40 percent of the total number of students, who took part in the online questionnaire, did not register and make a purchase at the online bookstore. Without the registration and purchases, the system was unable to determine the user’s preferences and therefore could not create a model of the user and generate a recommendation because of a cold start problem (Schein et al., 2002). This group of students was excluded from the final results of the Hadoop-based recommendation on the LMS Moodle platform. Other students, which had access to e-books for download from their online student services from the IS of the educational institution, and which have made purchases at the University online bookstore, and also borrowed books from the University library, met the requirements for the evaluation survey.
No. Question
Table V. An online questionnaire for gathering data from students based on their impressions
1 Do you borrow books from the University library? (a) Yes; (b) No 2 During the 2016/17 school year, how many books did you borrow? Depending on the previous question: (a) o5; (b) ⩾ 5 3 On the LMS Moodle, do you get a book recommendation? (a) Yes; (b) No 4 Please evaluate the received recommendation on the LMS Moodle platform based on Hadoop recommender (If the answer to the question No. 3 is yes): (a) Completely inadequate; (b) Somewhat inadequate; (c) Neutral; (d) Somewhat adequate; (e) 1Completely adequate 5 Based on the selected subjects in the school year 2016/17, in the information system of the educational institution, on your online student service, do you have available e-books to download? (a) Yes; (b) No 6 How many e-books are available for download through your online student service? Depending on the previous question: (a) o5; (b) ⩾ 5 7 Have you registered and made a purchase on the University online bookstore? (a) Yes; (b) No 8 How many books did you buy on the University online bookstore? Depending on the previous question: (a) o5; (b) ⩾ 5 9 When you sign into the University online bookstore, do you get a book recommendation? (a) Yes; (b) No 10 Please evaluate the received recommendation on the University online bookstore based on collaborative filtering (CF) (If the answer to the question No. 7 is yes): (a) Completely inadequate; (b) Somewhat inadequate; (c) Neutral; (d) Somewhat adequate; (e) Completely adequate
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The results of the questionnaire data are shown in Table VI. They show that – on the basis of the examined participants and their impressions, with the results of the survey analysis (x̅ – mean grade, from 1 to 5 and δ – standard deviation) and by the integration of multiple sources of different data – the system generates more appropriate recommendations, although there are small differences. In all, 24.6 percent of students rated the Hadoop recommendation completely adequate with a mean grade of 3.38 and a standard deviation of 1.25, while 21.1 percent of students have rated the recommendation of the University online bookstore completely adequate with a mean grade of 3.17 and a standard deviation of 1.28. Answers to questions related to the recommender system show that students’ slightly favor the Big Data recommendation that was integrated into the LMS Moodle platform and the Hadoop framework.
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Discussion According to the Lapkin report in Gartner (2012) “Big Data is not just about MapReduce and Hadoop. Although many organizations consider these distributed processing technologies to be the only relevant Big Data technology, there are alternatives. In addition, many organizations are using these technologies for more traditional use cases, such as preprocessing and the staging of information to be loaded into a data warehouse.” Such alternatives for parallel database systems for parallel processing and data analysis (e.g. Vertica, Teradata, Netezza, SQL Server, Greenplum, ParAccel) are expensive, difficult for administration, and have deficiency in fault tolerance and processing speed for longrunning queries (Pavlo et al., 2009; Sakr, 2016). In practice, Hadoop as an open-source project has achieved great success, with increasing momentum in research and development in educational and business domains. This technology, with modules of importance and their combination, has enabled even small companies to collect and analyze Big Data in order to gain a competitive advantage. Hadoop as open-source software provides a tool to process vast amounts of data easily and cost-effectively (Sakr, 2016). On the flip side, organizations need a data scientist to establish a workflow and fully utilize the advantages of Big Data analytics. Further, poorly identified input data will result in poor quality results regardless of the reliability of the Big Data solution. The major challenge in building a smart library is the analysis of a large amount of data that can be carried out in parallel processing through the Hadoop ecosystem. Proper analysis of the multiple data of differential sources integrated into the smart library is not only useful for increasing the efficiency and economic benefit of the organization but is also effective in additionally motivating users by providing hedonistic values. The obtained results show that the proposed system positively influences the performance parameters of the library by increasing the number of borrowed books and the perception of students, which indicate that such a system can be applied as an integral part
Question Please evaluate the received recommendation on the LMS Moodle platform based on Hadoop recommender Please evaluate the received recommendation on an the university online bookstore based on collaborative filtering (CF)
Completely Somewhat Somewhat Completely adequate adequate Neutral inadequate inadequate (%) (%) (%) (%) (%)
x̅
δ
24.6
22.8
26.3
19.3
7.0
3.38 1.25
21.1
19.3
24.6
26.3
8.8
3.17 1.28
Table VI. Descriptive statistics of students (n ¼ 132) based on their impressions with means and standard deviations related to questions No. 4 and No. 10
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of learning processes making the approach particularly suitable for application in educational institutions. By using the proposed data analysis system, librarians can achieve Big Data smart library operation for the coming generations. Conclusion The objective of this research is focused on the proposal of a solution for managing large differential data from multiple sources in smart libraries based on the Hadoop ecosystem. With integrating recommender systems to the smart library in the Big Data environment, satisfaction value for the users and the unique features for library management are proposed. The limitations of this study and the implications for research and practical use are presented in the following sections. Limitations Before the discussion of the possible limitations, it should be noted that most business systems are aware of the fact that the application of Big Data technology is inevitable, but are still trying to delay its implementation. Clearly, Big Data analytics requires the engagement of Data Scientists (Davenport and Patil, 2012) whose role is now significantly changing in library business systems. In order to achieve a competitive advantage in the Big Data environment, libraries need to implement a Big Data framework and the appropriate modules and tools that will increase customer satisfaction and long-term trust (Ratledge and Sproles, 2017). This paper presents a practical application of a Big Data recommender system and the first step of creating fully operational smart library system. The loading and processing of four data sources were carried out. The obtained results were visualized and evaluated. The implemented storage architecture has limitations and disadvantages when it comes to large-scale distributed systems. Required applications for running on highly scalable clusters of computers are server-side flow, data access optimization (Ishii and de Mello, 2012), and improved performance for data replication, distribution, migration, and access to parallelism (Philip Chen and Zhang, 2014). In order to improve performance and exploit the full potential of the proposed Big Data model in the smart library ecosystem, future integration with multiple (i.e. IoT and social media network) sources is needed. Also, data sparsity (Guo et al., 2014) remains an issue. Implications for research The infrastructure of the proposed ecosystem applicable in library management at the data set layer has the ability to manage both, relational and non-relational structures, enabling decision support systems and it can manage data in real-time using streaming tools and modules for interoperability of internal and external data sources. It also provides analytical applications for prediction analysis and interactive research of pooled data from the different sources. The platform also needs to support Data Scientists and researchers in managing real-time decision making. Implications for practice The rapid spread and expansion of data and different sources has created new approaches to their processing and a new channel for ubiquitous information access in libraries. The Hadoop framework is a powerful tool for solving large data management problems in smart environments. Big Data technology has a fundamental role in information management and in the context of knowledge discovery and library operations improvements. The proposed ecosystem has the capability to improve library operations for several reasons: ability to collect data from multiple different sources, reliability and fault tolerance
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in comparison to traditional data management systems, ability to store personal preferences and user attributes important for further predictions, analyzes content to find similarity from multiple sources, addresses the issue in the context of simultaneous access to multi-tier data from multiple sources, scales resources that deal with managing a larger amount of data, analyzes and processes data using modern Big Data technology, sharing of insights throughout of IT systems of the organization. The practical implications of this work also contribute a hedonic value (Shen et al., 2013) obtained using the recommendation system in smart libraries in a large data environment that will not only provide users useful and highly personalized contents but will also build long-term customer trust by generating a list of recommendations with greater precision. Further work The implementation of a smart library system with integrated IoT technologies and social media data, requires further research of the realized Big Data ecosystem that focuses on the following issues: Integration of data from social media platforms for real-time analytics; integration with the IS of the educational institution for developing and enabling a fully operational smart library system; IoT integration with distributed sensors in the library and how they generate data that is further processed in the proposed Big Data model in the Hadoop ecosystem in the most efficient way. References Adomavicius, G. and Tuzhilin, A. (2005), “Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions”, IEEE Transactions on Knowledge and Data Engineering, Vol. 17 No. 6, pp. 734-749. Ahrens, J., Hendrickson, B., Long, G., Miller, S., Ross, R. and Williams, D. (2011), “Data-intensive science in the US DOE: case studies and future challenges”, Computing in Science and Engineering, Vol. 13 No. 6, pp. 14-24. Amato, F., Moscato, V., Picariello, A. and Piccialli, F. (2017), “SOS: a multimedia recommender system for online social networks”, Future Generation Computer Systems, available at: https://doi.org/10. 1016/j.future.2017.04.028 (accessed April 23, 2017). Balabanović, M. and Shoham, Y. (1997), “Fab: content-based, collaborative recommendation”, Communications of the ACM, Vol. 40 No. 3, pp. 66-72. Barnaghi, P., Wang, W., Henson, C. and Taylor, K. (2012), “Semantics for the Internet of things: early progress and back to the future”, International Journal on Semantic Web and Information Systems IJSWIS, Vol. 8 No. 1, pp. 1-21. Bekkerman, R., Bilenko, M. and Langford, J. (2011), Scaling up Machine Learning: Parallel and Distributed Approaches, Cambridge University Press, New York, NY. Bernardes, D., Diaby, M., Fournier, R., FogelmanSoulié, F. and Viennet, E. (2014), “A social formalism and survey for recommender systems”, ACM SIGKDD Explorations Newsletter, Vol. 16 No. 2, pp. 20-37. Beyer, M.A. and Laney, D. (2012), “The importance of ‘big data’: a definition”, Gartner, June 21, available at: www.gartner.com/id=2057415 (accessed May 15, 2017). Bhat, W.A. (2018), “Long-term preservation of big data: prospects of current storage technologies in digital libraries”, Library Hi Tech, available at: https://doi.org/10.1108/LHT-06-2017-0117 Bizer, C., Heath, T. and Berners-Lee, T. (2009), “Linked data-the story so far”, International Journal on Semantic Web and Information Systems, Vol. 5 No. 3, pp. 1-22. Boyd, D. and Crawford, K. (2012), “Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon”, Information, Communication, & Society, Vol. 15 No. 5, pp. 662-679. Buckland, M.K. (2017), “Library technology in the next 20 years”, Library Hi Tech, Vol. 35 No. 1, pp. 5-10.
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Burnap, P., Rana, O., Williams, M., Housley, W., Edwards, A., Morgan, J. and Conejero, J. (2015), “COSMOS: Towards an integrated and scalable service for analyzing social media on demand”, International Journal of Parallel, Emergent and Distributed Systems, Vol. 30 No. 2, pp. 80-100. Buyya, R., Calheiros, R.N. and Dastjerdi, A.V. (2016), Big Data Principles and Paradigms, Elsevier Inc., Morgan Kaufmann, Cambridge, MA.
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Appendix 1
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Figure A1. Ambari module and the results of the executed query in order to determine the most recommended books in the Hadoop ecosystem
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Appendix 2
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Figure A2. Ambari module and the results of the executed query
Notes: Number of times that the recommended books have been borrowed from the University library in 2016. Before the activation of a Big Data ecosystem for the smart library
Appendix 3
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Notes: Number of times that the recommended books have been borrowed from the University library in 2017. When the prototype of the Big Data ecosystem for the smart library has been enabled Corresponding author Aleksandar Simović can be contacted at: [email protected]
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Figure A3. Ambari module and the results of the executed query
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A STUDY ON IMPLEMENTATION OF SMART LIBRARY SYSTEMS USING IOT
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A study on implementation of smart library systems using IoT Conference Paper · December 2017 DOI: 10.1109/ICTUS.2017.8286003
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A study on implementation of Smart Library Systems using IoT Imran Ahmed, Jitendra Pandey, Syed Imran Ali Kazmi, Muhammad Sohail Hayat Middle East College, Muscat, Sultanate of Oman
[email protected], [email protected], [email protected], [email protected] Abstract—The research is intended to design a smart library management application for the libraries in Oman. The library is one of the important parts in any educational organization. Although, library has a system, but the library needs to implement a new management system in order to replace the existing system by introducing the new system. There are many reasons why the library staffs have to implement another system, which are: loss a lot of information on the library books. The loss of data about the books Difficulty in tracking down the details of the library transactions due to a slow system Difficulty in updating the information on regular basis. Proposed research project is to proposed smart solution for libaries in oman by designed an application which will be called as Smart library Management System by using the concepts of Radio Frequency Identification (RFID) and Mobile(IoT). The new system will manage and control all the information of the library and solve the above mention problems and as well as provide several benefits for the staff & students. This application includes several forms which will be used by the library staff and students. Keywords— IoT, Smart Library, RFID
I. INTRODUCTION There are different problems that library staff face at the library, but the main problem faced by the staff is• To track books transaction information (electronic data) about the number of books that is being used by the students and teachers on day to day basis from the library. • It is difficult for library staff to know the exact number of books loosed by the library due to the present books handling issues which are sometime manual and show performance of the existing system. • Basically, the library staff cannot deal with the above two problems, and cannot provide correct information about the books for the staff and students as they find it difficult to update the information on regular basis. • In the existing system at the library the physical book tracking is always an issue as sometimes students pick the books from shelf’s and doesn’t put them back in proper place. Although, the library currently uses an application to manage the important information about the books, the existing system is very slow and is not so efficient in the library. The manual system doesn’t solve all other problems that are faced by the library staff. However, it being complex, the library needs to replace the manual system by means of a new application. As it is inaccurate in tracking the books and seeing whether they are issued or they are in library leading to a decrease efficiency of the system day after day and they cannot also
organize the system to serve the staff and students well. Added to that is the fact that data and information are repeated significantly. Hence it takes a lot of time and effort to solve this problem in the system. Also, it is important to manage the staff and students’ time effectively in doing the library transactions. This is increasing day after day and therefore there is a need to provide the best application, which needs great concentration and should be quick at the same time. The library staff faces difficulty in managing all these especially with a slow system as the college library is a general library that has several different sections for the books. Returning to the main problem, namely loss the information about the books, it leads to more and more problems like making the process for the library staff very slow. Also, staff and students take more time in doing the library transactions. For these reasons, the staff and students do not go to the library and take advantage of books. All the above noted problems require a suitable solution, which to design an excellent application for the library. All their requirements will be fulfilled by the new system.. II. METHODOLOGY
In order to achieve the main objective of proposed research, we have decided to use research methodology. Research consist of defining and redefining difficulties, framing proposition or recommended results; gathering, forming and appraising data; making suppositions and getting conclusions; and at last carefully testing the conclusions to determine whether they fit the articulating hypothesis. In research methodology there are many different kinds of methods. Authors have opted to use Quantitative-Qualitative method to obtain the main aim of proposed research. Quantitative research is based on the measurement of quantity or amount. Qualitative research, on the other hand, is concerned with qualitative phenomenon. Quantitative has only one phase i.e. Phase 1 although qualitative contains three phases i.e. Phase 2, phase 3 and Phase 4. In Phase 1 literature review is done on different library system management and RFID system and conduct a survey and research on library management system in Oman to integrate in the proposed solution.
In Phase 2 Design frameworks: framework is Excellent 72% Good 12% designed with all functionalities which define the relation between smart Library management application, RFID and IoT. In Phase 3 Development and Experiment: expected product is developed i.e. database was prepared to store user details & book details which are easily traceable and a smart shelf and RFID system is built to track and check-in-checkout the required book Figure :2 and simultaneously conduct various experiments and testing to meet the required outcome of this research. Figure :Data Analysis-1 III. PROPOSED FRAMEWORK
Fair 12% Poor 2%
The above graph shows how students rate the library services. As seen in the graph 72% of students said that the library staff services are excellent. 12% of the students said that the library services are good. That is equal to the percentage of the students who answered that the library services are fair. Lastly, 2% of the students said
Figure :1 IV. ANALYSIS
According to (Shahid, Syed Md., 2005), Radio Frequency Identification (RFID) allows an item. RFID is a broad term for technologies that use radio waves to automatically identify people or objects [3]. It can used this technology in library to circulation operations and theft detection systems and helps library staff reduce valuable staff time spent scanning barcodes while charging and discharging items. According to (Saranya, C, 2014), Web services are intended for realizing, storing, processing and disseminate data from environmental resources. it has RFID technology for student identification and sending precise information to the student’s smart phone. Following are the quantitative analysis as the primary study of this project (Gosling, Vazire, Srivastavab and John, 2004). Graphs for each question listed in the questionnaire will be analyzed like following: 1. How do you rate the library services?
that the library services are poor. As the graph show most of the students are satisfied about the MEC library staff services. 2. The library staff completes their work efficiently?
Strongly Agree 72% 6%
Agree 20%
Disagree
Strongly disagree 2% Figure :3
This graph shows what students think about the library staff complete their work efficiently. As seen in the graph, 72% of the students strongly agree that the library staff perform their work very
Figure :5
well. 20% of students agree that the library staff do their work as required. Added to that, 6% of the students strongly disagree that the library staff complete their work efficiently. 5% of students feel that the library staffs do not complete their work efficiently. From the answers of the students, most of the students are satisfied with the performance of
This graph shows if the students have to spend a lot of time waiting to borrow the book. It also shows if the students think that the library needs to change the current way. 76% of the student said they lose their time waiting to borrow the book, which reduces the efficiency of the library service. On the other hand, 24% of students said they do not lose
library staff. 3. Are you satisfied with the service level of the
their time waiting to borrow the book. 5. Are you satisfied with the cost of book
library? Yes 92%
when you lose it?
No 8%
Yes 12%
No 88% Figure :6
Figure : 4
student's
The above graph illustrates if the students are
satisfaction level of the MEC library services. As
satisfied about the cost of books in the library. As
we seen, 92% of the students are satisfied about
we seen in the graph, 88% of the students said they
library services. On the other side 8% of the
are not satisfied with the cost of books. Only 12%
students are dissatisfied with the level of staff
of the students are satisfied with the cost of books.
service at the library. It is very clear in the graph
Therefore, we can conclude from the percentages
above that most of the students are satisfied with
that most of the students are not satisfied with the
the staff service level of the MEC library.
books cost in MEC library.
4. Did you have to spend a lot of time waiting until
6. Do you think that the library needs to replace the
to borrow the book in the library?
existing system by a new application?
The
above
graph
Yes 76%
illustrates
No 24%
the
This research helps a lot to any organization. It reduces the man power need for the odd jobs. All work can be done by machine. In the long run its very efficient and helpful.
Yes 80%
No 20%
ACKNOWLEDGMENT The heading of the Acknowledgment section and the References section must not be numbered. Causal Productions wishes to acknowledge Michael Shell and other contributors for developing and maintaining the IEEE LaTeX style files which have been used in the preparation of this template. To see the list of contributors, please refer to the top of file IEEETran.cls in the IEEE LaTeX distribution. REFERENCES
Figure :7
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V. IMPLEMENTATION AND DESIGN
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Figure :8
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Here by all the components were connected according to the component description and working and block explained in the section below. TX is connected to RFID reader RX is connected to microcontroller. The data from the RFID goes to the 8051 microcontroller and is processed there. The output is then shown to the LCD. VI. CONCLUSIONS
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ADVANCING LIBRARY CYBERINFRASTRUCTURE FOR BIG DATA SHARING AND REUSE
Information Services & Use 37 (2017) 319–323 DOI 10.3233/ISU-170853 IOS Press
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Advancing library cyberinfrastructure for big data sharing and reuse Zhiwu Xie a,∗ and Edward A. Fox b a
University Libraries, Virginia Polytechnic Institute and State University, 560 Drillfield Drive Blacksburg, VA 24061, USA b Department of Computer Science, Virginia Polytechnic Institute and State University, 114 McBryde Hall, M/C 0106, Blacksburg, VA 24061, USA Abstract. Data-intensive science presents new opportunities as well as challenges to research libraries. The cyberinfrastructural challenge, although chiefly technological, also involves social-economic and human factors, therefore requires a deep understanding of what roles research libraries should play in the research lifecycle. This paper discusses the rationale and motivations behind a research project to investigate effective library big data cyberinfrastructure strategies. Keywords: Data management, Big Data, cyberinstrastructure, data sharing, data reuse
1. Introduction As a key component of the nation’s knowledge infrastructure, libraries must continuously reinvent themselves with the emergence and the establishment of new discovery paradigms. The recent wave of data-intensive science has motivated many high-profile library big data projects, notably the ambitious plan to archive all tweets at the Library of Congress [13], the heterogeneous and geographicallyreplicated archival storage known as the Digital Preservation Network (DPN) [15], the data mining facility at the HathiTrust Research Center (HTRC) [12], and the metadata hubs developed at the Digital Public Library of America (DPLA) [4] and the SHARE initiative [14]. Many more are being developed or being planned. The common theme of these library projects is to handle high volumes of data. Since the volume usually exceeds the currently deployed capacity of the typical library cyberinfrastructure (CI), we must have more storage, processing capacity, and network bandwidth, to name a few requirements. It would be prohibitively expensive to build local, proprietary capacities at each library. Fortunately a wide range of shared CI options exist, including, but not limited to: 1) Institutional high-performance computing (HPC), high-throughput computing (HTC) and storage facilities, e.g., Indiana University’s Big Red II, Virginia Tech’s BlueRidge, etc.; 2) National HPC, HTC, and storage facilities, most notably XSEDE resources [16]; 3) National research clouds such as Chameleon Cloud, CloudLab, Open Science Data Cloud, etc.; 4) Commercial clouds, such as Amazon Web Services (AWS), Rackspace, etc. These shared resources, especially the commercial clouds, have drastically lowered the barrier to entry for the big data game. This has become especially evident in the Information Technology (IT) industry, * Corresponding
author. E-mail: [email protected].
0167-5265/17/$35.00 © 2017 – IOS Press and the authors. This article is published online with Open Access and distributed under the terms of the Creative Commons Attribution Non-Commercial License (CC BY-NC 4.0).
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where even a small start-up can handle high volumes of data today. Indeed, most library big data projects we surveyed are built on such shared CI resources and do not require significant initial investment. It is therefore a fallacy to assume that only libraries with deep pockets are qualified to provide big data services. However, operating library big data services on shared CI resources is far from turnkey. Although some general guidelines exist [3,7,9] it is not always clear what we can learn from the IT sector’s success in developing big data services. Are big data services to be operated at a research library the same as or different from those provided by common commercial services? What are the key technical challenges? What are the key performance characteristics? What are the monetary and non-monetary (time, skill set, administrative, etc.) costs? Are there any cost patterns or correlations among the CI options? What are the knowledge and skill requirements for librarians? To answer these questions, we must seek better understanding of why and how research libraries develop big data services. 2. Big data, small science, and libraries Big data used to be closely associated with big science, which in turn is characterized by big organizations and big budgets [2,5,6]. This is no longer true. The fast advance and ubiquitous availability of sensing technologies, the Web, and the Cloud have erased the data volume boundary separating big and small science. For example, the 1000 Genomes project [1] produced 200 TB of data from 2008 to 2012. The Sloan Digital Sky Survey [18] produced about 130 TB of raw and derivative data over eight years in phases I and II. In contrast, the sensors installed in Virginia Tech’s Goodwin Hall alone can collect 200 TB per year at moderate sampling rate, and a handful of Amazon EC2 instances can gather as much web data in weeks. Neither the big organization nor the big budget is a must-have to conduct data-intensive research. The Goodwin Hall project was started by two faculty members and a small lab. Crawling and analyzing web data is so affordable today that even a student can initiate his or her own web analytics project. The leveling of the big data playing field makes it possible for many more small science projects to take advantage of big data. Organization-wise, these projects usually emerge from the ground up, with user communities naturally forming, growing, and self-organizing around the data connected with their own needs, use cases, and perspectives. However, these project teams usually lack experience and expertise to effectively extract values from the large data sets, therefore opening up the opportunities for the research libraries to build and offer new value-added services. 3. Use and reuse driven big data management Even if cost is not a major concern, a traditional digital library can hardly deliver services suitable for data-intensive research, especially for small science projects. For example, a download link is no longer sufficient to provide effective access to terabytes of research data, because randomly-moving data to a remote site may take too long, and therefore clog the reuse workflow. When the data volume grows larger, it also becomes more difficult to justify a dark archive or traditional preservation approaches, e.g., making multiple copies and storing them among geographically-distributed locations. The ingestion and dissemination processing may involve much time and processing overhead, challenging the conventional wisdom that data may need to be stored in a different format, layout, or logical unit from when they are in use. In contrast to the more immediate user needs to effectively access data, these traditional digital
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library functions become relatively trivial. Moreover, libraries are increasingly expected to deliver not just the raw data but also knowledge extracted from them, e.g., running user-specified algorithms against preserved data, pushing customized information from a metadata hub, or analyzing web archives. It is therefore imperative to manage data with the use and reuse-driven approach [17]. Using the DCC Curation Lifecycle Model terms [8] developed by the Digital Curation Centre (DCC) in the UK, the library big data service should focus more on facilitating data use and reuse instead of spreading the library resources evenly among storage, preservation, resource description, and various transformations, each for its own purpose. Facilitating data use and reuse should become the driving force behind other activities. For example, under this approach, the storage layout should be optimized towards more efficient reuse, with multi-copy preservation considered a side effect of the replication deployed to increase access bandwidth. 4. Library big data service patterns There will be many different library big data services, and each may be operated on any of the shared cyberinfrastructure options listed in Section 1. However, without appropriate categorization, it would be non-trivial trying to mix and match them to achieve optimal performance. We therefore conduct an environmental scan to extract service patterns. Conceptually, we can draw three distinct, although not mutually-exclusive, service patterns, schematically shown in Fig. 1: 1) The Bridge Pattern clearly separates the data storage and data processing in different facilities, and answers sporadic, on-demand, and sometimes user-specified computing needs by moving data from storage to processing nodes through the network link between them. The Digital Preservation Network (DPN) nodes [2], despite being primarily concerned with data storage, may be considered special cases of the Bridge Pattern. This is because the data ingestion, validation, periodic fixity checking, and refreshing are indeed on-demand data processing performed at compute nodes away from where the data is stored. 2) The Network Pattern, as exemplified by warcbase [11] – an open-source platform for managing web archives built on Hadoop and HBase – features a much tighter integration between data storage and processing. Typically involving a Hadoop cluster, this pattern uses a large number of interconnected nodes, each serving both as a storage and processing unit. These nodes replicate, balance, and co-optimize both storage and computing across the interconnections. The Network Pattern excels at MapReduce types of computation and can sustain high processing loads. However, the
Fig. 1. Three patterns for library big data services.
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initial data loading stage is known to be a bottleneck [10]. As a result, data tend to be “sticky” to the CI. Once loaded, the data usually stay put. 3) The Hub Pattern includes the Digital Public Library of America (DPLA) [4] and SHARE Notify [14], both specialized as metadata hubs. This service pattern continuously draws live data from potentially many sources, undertakes necessary processing, and then disseminates processed information to potentially large numbers of data consumers. It has higher quality of service (QoS) requirements, since downtime may lead to permanent data loss. In addition to the performance requirements on the computing and storage nodes, it also requires stable network connections to the external systems upon which it depends. 5. Summary With an emphasis on big data sharing and reuse, we are conducting a research project aiming to develop an evidence-based, broadly-adaptable Cyberinfrastructure (CI) strategy to operate digital library services. The strategy will equip research libraries with knowledge and techniques to leverage shared CI resources and balance their desires, needs, and constraints with a clear understanding of the tradeoffs. Acknowledgement This work was partially supported by IMLS LG-71-16-0037-16. About the authors Zhiwu Xie is a professor and directs the digital library development team at Virginia Tech Libraries. He leads the development of the Goodwin Hall Living Lab data management system, IMLS ETDplus Workbench, VTechData, and UWS through transactional web archiving, among others. He served on technical committees of Fedora, APTrust, PREMIS, ResourceSync, and Altmetrics Data Quality efforts. His research extensively utilizes all types of CI options summarized in this paper, and has been supported by Mellon, IBM, Amazon, USGS, and NSF XSEDE. Edward Fox is a professor in the Department of Computer Science, Virginia Tech. A Fellow of IEEE, he is a senior computer scientist and digital library innovator. He chaired the IEEE Technical Committee on Digital Libraries, ACM SIGIR, and the JCDL steering committee, and has been (co-)PI on one hundred and twenty-four research grants/contracts, (co-)authored eighteen books, one hundred and twenty-six journal/magazine articles, forty-nine book chapters, two hundred and nineteen refereed conference/workshop papers, seventy-four posters, and over one hundred and fifty other publications/reports, with an h-index of over fifty-six. References [1] 1000 Genomes Project Consortium, A map of human genome variation from population-scale sequencing, Nature 467(7319) (2010), 1061–1073. doi:10.1038/nature09534. [2] J. Bicarregui, N. Gray, R. Henderson, R. Jones, S. Lambert and B. Matthews, Data management and preservation planning for big science, International Journal of Digital Curation 8(1) (2013), 29–41. doi:10.2218/ijdc.v8i1.247. [3] Cyberinfrastructure Council, Cyberinfrastructure Vision for 21st Century Discovery, National Science Foundation, Arlington, VA, 2007, Report No.: NSF-2007-28.
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[4] Digital Public Library of America [Internet], [cited 2017 April 10]. Available from: https://dp.la/. [5] N. Gray, T. Carozzi and G. Woan, Managing research data in big science, JISC, 2012 July, Available from http://arxiv. org/abs/1207.3923. [6] P.B. Heidorn, Shedding light on the dark data in the long tail of science, Library Trends 57(2) (2008), 280–299. doi:10. 1353/lib.0.0036. [7] G. Henry, Core infrastructure considerations for large digital libraries, Council on Library and Information Resources, Digital Library Federation, 2012. [8] S. Higgins, The DCC curation lifecycle model, International Journal of Digital Curation 3(1) (2008), 134–140. doi:10. 2218/ijdc.v3i1.48. [9] S.J. Jackson, P.N. Edwards, G.C. Bowker and C.P. Knobel, Understanding infrastructure: History, heuristics and cyberinfrastructure policy, First Monday 12(6), 2007 June 4. [10] Y. Kargın, M. Kersten, S. Manegold and H. Pirk, The DBMS – your big data sommelier, in: 2015 IEEE 31st International Conference on Data Engineering (ICDE), IEEE, 2015, pp. 1119–1130. doi:10.1109/ICDE.2015.7113361. [11] J. Lin, M. Gholami and J. Rao, Infrastructure for supporting exploration and discovery in web archives, in: Proceedings of the 23rd International Conference on World Wide Web, ACM, 2014, pp. 851–856. [12] B. Plale, R. McDonald, Y. Sun, I. Kouper, R. Cobine, J.S. Downie et al., HathiTrust Research Center: Computational Access for Digital Humanities and Beyond, Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, ACM, New York, NY, USA, 2013. [13] M. Raymond, The Library and Twitter: An FAQ, | Library of Congress Blog [Internet], 2010 [cited 2017 April 10]. Available from http://blogs.loc.gov/loc/2010/04/the-library-and-twitter-an-faq/. [14] SHARE Internet [cited 2017 April 10]. Available from http://www.share-research.org/. [15] The Digital Preservation Network [Internet], [cited 2017 April 10]. Available from http://dpn.org/. [16] J. Towns, T. Cockerill, M. Dahan, I. Foster, K. Gaither, A. Grimshaw et al., XSEDE: Accelerating scientific discovery, Computing in Science & Engineering 16(5) (2014), 62–74. doi:10.1109/MCSE.2014.80. [17] Z. Xie, Y. Chen, J. Speer, T. Walters, P.A. Tarazaga and M. Kasarda, Towards use and reuse driven big data management, in: Proceedings of the 15th ACM/IEEE-CS Joint Conference on Digital Libraries, ACM, 2015, pp. 65–74. doi:10.1145/ 2756406.2756924. [18] D.G. York, J. Adelman, J.E. Anderson Jr., S.F. Anderson, J. Annis, N.A. Bahcall et al., The sloan digital sky survey: Technical summary, The Astronomical Journal 120(3) (2000), 1579. doi:10.1086/301513.
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AN EMPIRICAL STUDY ON THE FACTORS INFLUENCING MOBILE LIBRARY USAGE IN IOT ERA
Library Hi Tech An empirical study on the factors influencing mobile library usage in IoT era Xiwei Wang, Jiaxing Li, Mengqing Yang, Yong Chen, Xiaobo Xu,
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An empirical study on the factors influencing mobile library usage in IoT era Xiwei Wang
Mobile library usage in IoT era
605
Department of Management, Jilin University, Changchun, China
Jiaxing Li and Mengqing Yang Jilin University, Changchun, China
Received 22 November 2017 Revised 27 March 2018 Accepted 9 April 2018
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Yong Chen Texas A&M International University, Laredo, Texas, USA, and
Xiaobo Xu American University of Sharjah, Sharjah, UAE Abstract Purpose – The purpose of this paper is to construct an information ecology factor-TAM model to explore how information, people, and information environment impact mobile library users’ behavioral intention. Design/methodology/approach – An empirical study based on an information ecology factor-TAM model. Findings – The results of model test indicate that information, people, and information environment affect users’ behavioral intention to use mobile library in the information ecology environment. This paper provides a new theoretical perspective to understand users’ behavior of mobile library. It also provides practical guidelines to promote the usage of mobile library. Originality/value – Internet of Things makes digital library one important channel for people to obtain information. Mobile internet makes it possible to access a variety of information anytime and anywhere. With the rapid grow of the number of mobile internet users, the usage of mobile library does not grow correspondingly. This paper explores the reasons. Keywords Internet of Things (IoT), Information ecology, TAM, Behavioural intention, Mobile library Paper type Research paper
1. Introduction The internet has changed the way people obtain information (Thanuskodi, 2012). Because reading materials, including text document, image, audio, video and software, are getting more fragmented (Fortino et al., 2014), libraries have been integrating the internet and utilizing new tools and methods to provide users information (Abdoulaye and Majid, 2000), such as digital resources, mobile service, notification service and ubiquitous service on the basis of traditional service (Fortino et al., 2014). People who used to visit libraries for specific information are now able to find the same information online (Li, 2013). Accordingly, traditional libraries are transforming into digital ones, aiming to have the abilities of collecting, managing, preserving, resources; integrating access to various information sources; and providing specialized services to users fast and at low maintenance cost (Christophides et al., 2000). Built on radio frequency identification (RFID), near field communication, and sensor networks, the Internet of Things (IoT) connects heterogeneous and massively decentralized devices, including domestic appliances, actuators, sensors, and wireless communications platforms, to the internet and facilitates their communication to accomplish some objective (Castellani et al., 2011; Cirani et al., 2015; Fazackerley et al., 2015; Finogeev and Finogeev, 2017; Li et al., 2013, 2015, 2018; Sivieri et al., 2012; Whitmore et al., 2015). This research is supported in part by the National Natural Science Foundation of China (NSFC) under Project (71203074) and the 985 Project of Jilin University.
Library Hi Tech Vol. 36 No. 4, 2018 pp. 605-621 © Emerald Publishing Limited 0737-8831 DOI 10.1108/LHT-01-2018-0008
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According to Xu et al. (2014), IoT has four layers, namely sensing layer, networking layer, service layer, and interface layer. Based on the Raspberry Pi, BeagleBone and BeagleBone Black, IoT solutions offer cost effective, versatile and uncomplicated platforms for rapid application development (Kruger and Hancke, 2014). Mobile devices can be used to access a variety of information anytime and anywhere. With the fast development of wireless network and mobile devices in recent years, mobile information service has been gradually integrated into human life. As a result, mobile internet applications has become a channel for people to access information. Smart phones and mobile technologies are changing the ways that we consume, distribute, and create information (Little, 2011). As Choy (2010) points out, students are increasingly rely on smartphones to access online services. Mobile internet offers them great flexibility and convenience in accessing rich digital resources and allows them to learn independent of time and location (Liu et al., 2010). As such, a large number of libraries have combined their services with mobile technology, resulting in the creation of mobile library (Huang et al., 2015). According to Gan and Song (2015), mobile library is the delivery of library services through wireless access using mobile devices to allow such activities as mobile searching and mobile reading. The service modes of mobile library include short message service, wireless application protocol and application for mobile terminals (APP) (Wei and Yang, 2017). Mobile library gives learners the convenience access to digital resources and library services (Gan et al., 2017; Wang et al., 2009). On the other hand, mobile library is seen by libraries as a positive way to improve their image and to meet the needs of a younger generation of library users who are increasingly interacting with services via mobile devices (Alfaresi and Hone, 2015). An ever-increasing number of people worldwide access the internet through their mobile devices daily (Aharony, 2014). According to a report released by the China Internet Network Information Center, the number of mobile internet users in China had reached 695 million by December 2016, accounting for 95.1 per cent of Chinese internet users (China Internet Network Information Center, 2017). However, the number of mobile library users is relatively low compared with the numbers of uses of other mobile application (Gan et al., 2017). In addition, Zhao et al. (2015) note that although mobile library applications have developed very fast and smoothly in China and many users have downloaded and tried to put them into use, few users use them continually. Recent literatures have applied the technology acceptance model (TAM), the theory of unified theory of acceptance and use of technology, and the theory of planned behavior to explore the reasons (e.g. Gan and Song, 2015; Gan et al., 2017; Joo et al., 2014; Liu et al., 2010). These studies mainly concern the acceptance intention and willingness to access library resources (Vassilakaki, 2014). This paper, however, argues that people is the core of the information ecosystem and that the information ecology theory can provide a new theoretical framework as well as a conceptual model for the adoption of mobile library from the perspective of user experience. The information ecology theory explains the harmonious development of people, information, and information ecology. It has been applied to explain individuals’ behavior in information architecture and organization. By combining the theory of information ecology and the TAM model, this paper aims to answer two questions: how do information, people, and information environment impact mobile library users’ behavioral intention? Is there a new way to develop mobile library? The rest of this paper is organized as follows. The second section presents the literature review of mobile library and information ecology. The third section discusses the research model and proposes hypotheses. The methodology is explained in the fourth section. The fifth section discusses data analytical results. The last section presents implications and conclusion.
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2. Literature review 2.1 IoT and libraries IoT has been widely applied in healthcare, supply chain, logistics, mining, transportation, firefighting, intelligent home, building automation, smart grids, smart city, energy management and asset tracking (Bi et al., 2018; Chen, 2017; Civerchia et al., 2017; Fang et al., 2014; Kim, 2017; Lai et al., 2017; Liu et al., 2017; Peretti et al., 2015; Rashid et al., 2016; Xu, 2011; Wang et al., 2014; Xu et al., 2014, 2018). In recent years, applications of IoT have expanded from industries to education. Colleges and universities are actively constructing wisdom campus based on IoT to ensure digital resources integration and sharing, including campus personnel identification, library management, campus ID, student management, teaching environment management in class, and the management of teaching instruments and equipment (Wang, 2014; Zhao, 2013; Zong et al., 2014). Wójcik (2016) notes that IoT technology might have the potential to be used in library services, similar to how it is implemented in the commercial sector. IoT will bring about a series of profound changes for libraries (Du and Liu, 2014; Sun, 2014), particularly transforming the libraries to smartened libraries or digital libraries (Xu, 2014). Li (2013) points out that many library services, such as self-borrowing and self-returning, smart inventory, intelligent query, combination of books and information system, can be achieved by IoT. Accordingly, e-resources and digital libraries are becoming increasingly important channels for obtaining information (Hu and Zhang, 2016). Some researchers have explored how IoT can be applied in libraries. For instance, Andersen (2002) examines the relationship between communication technologies and the Library and Information Science concept of knowledge organization from a medium-theory perspective. Liu and Sheng (2011) present the development direction of IoT in the field of library management and promotion programs. Ma et al. (2011) present an integrated management system with multilayer architecture based on IoT for managing study rooms in libraries. Sun (2014) designs the system of smart library. Sun et al. (2014) use IoT technology to mine, identify, organize and analyze the implied reader behavior to improve the library service, resources, services, and to achieve the optimum configuration. Based on IoT technology and humanized design, Yao and Song (2014) propose an intelligent control system for saving lighting energy in libraries. Wei et al. (2014) discuss the construction of smart library information management system based on cloud computing and IoT. Ma (2015) argues that network based on IoT is important for libraries and that it could be an effective method for improving the work of library. Cheng et al. (2016) present a corresponding handheld device client software to improve the efficiency of book search and management, and to save the manpower and material resources. 2.2 Mobile library Mobile technologies are characterized by their small size and portability. The concept of combining mobile service and library service was first introduced by scholars in the era of personal digital assistants (Huang et al., 2015). However, due to the immature of mobile devices and wireless communication technologies at that time, the adoption rate of mobile library was low. With the advancement of wireless networks and mobile devices, acquiring information anytime and at anywhere becomes convenient and fast. As a result, the majority of libraries combine wireless communication technologies with their services to develop mobile services compatible with mobile devices (Lai et al., 2014). As a new platform for knowledge sharing and learning, mobile library integrate subjects on the library mobile service chain, including the library, database provider, mobile technology provider, mobile network operator, and mobile terminal manufacturer (Zhao et al., 2015). Mobile library not only provides comprehensively integrate the library’s basic functions, but also provide intelligent services, such as location-based services, context awareness and quick response code scanning (Zhao et al., 2015). According to Wei and Yang (2017), the most commonly used mobile library services are
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LibraryWorld Online Patron Access (OPAC) retrieval, items borrowed, personal center, resource discovery and announcements. For users, mobile library allows them to check due dates, renew and reserve items, search items in the library effectively, as well as navigate and locate items easily (Paterson and Low, 2011). For libraries, they can shift from having a fixed location to becoming ubiquitous by combing mobile librarians, mobile patrons, and mobile content (Barnhart and Pierce, 2011). Compared with traditional library, mobile library free students from temporal and spatial limitations, enabling them to acquire library resources and services anytime and anywhere (Chang, 2013). For example, Chaoxing, a professional mobile reading platform/app, integrates the OPAC system, digital library portal, cloud sharing service system, information exchange and interaction platforms, and personalized services and is capable of providing users with more than one million e-books and innumerable newspapers, as well as domestic and foreign literature metadata (Hu and Zhang, 2016). Studies have been conducted to investigate the development of mobile library. For instance, Falk (2005) discovers that using RFID tags can increase the use of e-books and improve library circulation and maintenance. Paterson and Low (2011) examine the benefit of academic mobile library services using quantitative and qualitative data about students’ use of mobile devices. Canuel and Crichton (2011) assess how Canadian academic libraries responded to the rapidly evolving mobile environment, identify the service gaps, and suggest future development directions. Wang et al. (2012) investigate mobile web services in terms of due date reminder and renewal by using the Oriental Institute of Technology Library in Taiwan. Based on colleague students’ library requirements, Nowlan (2013) explores how to best construct a mobile site to suit the university community’s needs. Aharony (2013) explores whether librarians are familiar with and accept technological innovations. Seeholzer and Salem (2011) find that students are interested in using their mobile devices to interact with library sources and services, particularly using their smartphones for searching databases and the library catalog, as well as staying informed by the library staff. All these researches provide valuable insights of mobile library from multiple perspectives. 2.3 Information ecology The concept of information ecology was first proposed by Capurro (1989). Later, Nardi, and O’Day (1999) define information ecology as “a system of people, practices, values, and technologies in a particular local environment” (p. 1). They propose that the technologysupported human activity is more important than the technology by examining the relationship between IT and human in the local environment. Previous studies about the information ecology have been conducted from different perspectives. Detlor (2001) focuses on the influence of an organization’s information ecology or internal information environment on its electronic commerce initiatives and plans. Nam et al. (2013) apply the social science hyperlink analysis to examine the web ecology of the 2010 local elections in South Korea. Although the information ecology can be defined in various ways, it emphasizes the human factors more than the technological factors (Nam et al., 2013). Information ecology focuses on the interrelationships between people, technologies, and their information environment. However, people are situated at the core of the information ecosystem. Information ecology can be characterized by the following attributes: the integration of diverse types of information; the recognition of the evolutionary change; the emphasis on the continuous observation and description of how information is gathered, used, and shared; and the focus on people and their information behaviors (Davenport and Prusak, 1997). 2.4 Mobile library with the information ecology System and ecology are particularly useful for understanding the complexity of information management changes (García-Marco, 2011). During the recent years, the information
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ecology concept has been used to represent the converging of colliding media into a new multimedia landscape (Garcı´a-Marco, 2008). The rapid development of internet and mobile technology, users, and smartphones are three factors of the information ecology. They have gained new ecology characteristics during the mobile library development. Meeting users’ demands in the mobile devices environment and improving the user satisfaction have become a new challenge of the information ecology for mobile library.
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3. Model and hypotheses 3.1 TAM Based on the modified theory of reasoned action (Ajzen and Fishbein, 1980), Davis (1989) proposes the TAM which explains the influence of external variables on the user’s inherent belief, subjective attitude, and behavioral intention. It explores the users’ influential factors and behaviors to accept a new technology. In the TAM, perceived ease of use is the degree to which an individual believes that using a specific technology will be free from effort. Therefore, if an IT application is perceived as easier to use than another, users will be more likely to accept the application. Perceived usefulness refers to the extent that using a certain system would enhance the job performance (Davis, 1989). The TAM contains three basic relationships affecting the behavioral intention: perceived ease of use that impacts perceived usefulness, perceived ease of use that leads to behavioral intention, and perceived usefulness that affects behavioral intention (López-Nicolás et al., 2008). The TAM is shown in Figure 1. 3.2 Information ecology factor-TAM The TAM has been modified through the years and a number of studies have extended its basic framework. Venkatesh and Davis (1996) suggest that users’ computer self-efficacy affected perceived ease of use. Later, Venkatesh and Davis (2000) propose that computer self-efficacy, intrinsic motivation, and emotion affect ease of use. They also argued that subjective norm, voluntariness, image, job relevance, output quality, and result demonstrability impact user acceptance of systems. We adopt the TAM as basic model and information ecology factors as the external variables to analyze the user’s intention to use mobile library. The three external variables are information factor, information environment factor, and people factor. Since we mainly focus on the influence on behavioral intention to use mobile library, we omit “use behavior” of the TAM. The research model is shown in Figure 2. 3.3 Hypotheses 3.3.1 Information quality. Information quality emphasizes the usefulness and ease of use of information (Chiou et al., 2010). Usefulness of information presents the value of information. It is the first factor for the mobile library to appeal to users. It highlights the degree of Perceived usefulness
External factors
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Perceived ease of use
Behavioral intension to use
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Figure 1. TAM
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Figure 2. Information ecology factor—TAM
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H3b
People initiative
satisfaction of users’ information needs. Ease of use emphasizes the degree of difficulty when users access to the information they need. It also represents the maturity of the mobile library services. Information is the main service of mobile library so that its usefulness and ease of use will inevitably influence the perceived usefulness and perceived ease of use. Thus, we propose the following hypotheses: H1a. Information quality has a positive effect on perceived usefulness of mobile library users. H1b. Information quality has a positive effect on perceived ease of use of mobile library users. 3.3.2 Information environment quality. We divide the information environment into the internal environment and the external environment (Dickinger and Stangl, 2013). The internal environment refers to the environment of information transmission and access provided by the mobile library services, including service mode and service quality. The external environment refers to the external social environment which has an influence on user intention to use, including the crowd behavior, advertisement, etc. The information environment can build an effective communication method for “people” and “information.” Thus, we propose the following hypotheses: H2a. Information environment quality has a positive effect on perceived usefulness of mobile library users. H2b. Information environment quality has a positive effect on perceived ease of use of mobile library users. 3.3.3 People initiative. The core of information ecology is people (Nam et al., 2013). The people initiative emphasizes the experience and information needs of people from this perspective. An experienced user can find the advanced features of certain system through proficient use. Therefore, the veteran users are more likely to experience the usefulness and ease of use of a certain system. As an inner driving force of users, information need is the motivation for users to use mobile library. Thus, we propose the following hypotheses: H3a. People initiative has a positive effect on perceived usefulness of mobile library users. H3b. People initiative has a positive effect on perceived ease of use of mobile library users.
Mobile library usage in IoT era
3.3.4 Other hypotheses. We also propose the below hypotheses: H4. Perceived ease of use has a positive effect on perceived usefulness. H5. Perceived usefulness has a positive effect on use attitude. H6. Perceived ease of use has a positive effect on use attitude. H7. Perceived usefulness has a positive effect on behavioral intention to use.
611
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H8. Use attitude has a positive effect on behavioral intention to use. 4. Methodology 4.1 Questionnaire design Data were collected through a survey questionnaire to examine the hypotheses. The questionnaires were composed of 2 sections, one of which solicited background information about the respondents. The other section used questions to measure the model constructs. The participants (mainly university students) were requested to answer 21 questions using a five-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (5). We sent 250 questionnaires out and received 201 responses. The response rate is 80.4 percent. Because participants of this research are Chinese university students (including undergraduate and graduate students) and college staff, the sample age distribution is balanced and most people are between the ages of 18 and 40 (73.2 percent). In total, 48.3 percent participants were male and 51.7 percent were female. Most participants have used academic mobile library for more than six months (77.6 percent) means that our participants have enough experience. And more than half of the participants used academic mobile library to look for learning materials (51.7 percent), followed by reading (34.3 percent). Tables I summarizes demographic statistics. We either adopted or adapted measurement items from previous studies. All measurement the items are listed in Appendix.
Characteristics
Frequency proportion
Proportion (%)
97 104
48.3 51.7
Age Under 18 18-25 26-30 31-40 Above 40
33 51 57 39 21
16.4 25.4 28.4 19.4 10.4
Usage experience o 6 months 6-12 months 1 to 2 years W 2 years
45 73 43 40
22.4 36.3 21.4 19.9
69 104 18 10
34.3 51.7 9.0 5.0
Gender Male Female
Main purposes Reading Looking for learning materials Service query Others Note: n ¼ 201
Table I. Summary of demographic statistics
LHT 36,4
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612
4.2 Data analysis Because this research is a confirmatory research based on the theoretical support, in order to verify the causal relationship between potential variables, structural equation modeling has been used for simultaneous estimation of interdependent causal relationships through confirmatory factor analysis (CFA) and the hypothesized relationships among the constructs through the model testing. The AMOS version 21 was used in this paper. The convergent validity was assessed using three types of reliability indices: individual item reliability, composite reliability and the average variance extracted (AVE) from the latent variables. Factor loadings of all variables are reported in Table II and are higher than 0.60. The squared multiple correlation (SMC) represents the variance of a measured variable explained by a latent construct. In this study, most regression weights ( factor loadings ranging from 0.661 to 0.948) were highly significant with SMCs ranging from 0.437 to 0.899. The CR values of all variables are between 0.772 and 0.875. To evaluate the convergent validity of each construct, the AVE is acceptable when it is more than 0.5 (Hair et al., 2010). All AVE values ranging from 0.532 to 0.702 provided the evidence of convergent validity. Therefore, it suggested that all constructs in the model have adequate reliability and convergent validity. Discriminant validity is assessed by comparing the square root of AVE for a given construct with correlations between that construct and all others. In Tables III, the square root of every construct’s AVE value was provided by those bold numbers on the diagonal
Construct and item
Table II. Confirmatory factor analysis
Factor loading
SMC
CR
AVE
Information quality 0.875 0.702 Inf1 0.836 0.699 Inf2 0.923 0.852 Inf3 0.745 0.555 Information environment quality 0.837 0.636 Env1 0.717 0.514 Env2 0.948 0.899 Env3 0.703 0.494 People initiative 0.859 0.673 Peo1 0.805 0.648 Peo2 0.937 0.878 Peo3 0.702 0.493 Perceived usefulness 0.815 0.596 PU1 0.792 0.627 PU2 0.823 0.677 PU3 0.695 0.483 Perceived ease of use 0.790 0.559 PEU1 0.670 0.449 PEU2 0.869 0.755 PEU3 0.688 0.473 Use attitude 0.772 0.532 Att1 0.702 0.493 Att2 0.816 0.666 Att3 0.661 0.437 Behavioral intention 0.793 0.560 Int1 0.761 0.579 Int2 0.761 0.579 Int3 0.723 0.522 Notes: SMC, squared multiple correlations; CR, construct reliability; AVE, average variance extracted
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(from 0.717 to 0.794). The values should be higher than their correlations with the other constructs. The correlations between every construct and the other constructs were displayed off the diagonal. Therefore, discriminant validity was reached. 5. Analytical results We used AMOS 21.0 to examine the hypothesized relationships among the constructs through the model testing. Before testing hypotheses in the structural model, the model fit was tested in Table IV. Fit indices showed an appropriate fit for the overall structural model (w2 ¼ 203.400; NCI ¼ 1.162; RMSEA ¼ 0.028; GFI ¼ 0.915; NFI ¼ 0.900; CFI ¼ 0.984). Since the values of all fit indices satisfied their recommended thresholds, a good fit between the model and the data was observed. This study uses the maximum likehood method to estimate each path’s coefficient. The critical ratio of CR is equivalent to the t-test value. The estimate is significant at p ¼ 0.05 if the absolute value of CR is greater than 1.96; the estimate is significant at p ¼ 0.01 if the absolute value of CR is greater than 2.58. When the p-value is smaller than 0.001, we use a “***” symbol in the table. Otherwise, the p-value is shown directly. The final model’s model fit is shown in Table V. Based on the model fit results, the mobile library user behavioral intention factor model is refined (see Figure 3). Nine hypotheses are supported. Information quality has significant positive effects on PU ( β ¼ 0.324, t ¼ 4.129) and PEU ( β ¼ 0.179, t ¼ 2.099), supporting H1a and H1b. Information environment quality has significant positive effects on PU ( β ¼ 0.158, t ¼ 2.032) and PEU ( β ¼ 0.229, t ¼ 2.629), supporting H2a and H2b. However, H3a and H3b are not supported. PEU has significant positive effects on PU ( β ¼ 0.360, t ¼ 4.076) and Use attitude ( β ¼ 0.466, t ¼ 4.557), supporting H4 and H6. PU has significant positive effects on Use attitude ( β ¼ 0.328, t ¼ 3.411) and Behavioral intention ( β ¼ 0.200, t ¼ 5.254), supporting H5 and H7. Use attitude has a significant positive effect on Behavioral intention ( β ¼ 0.593, t ¼ 2.017), supporting H8. Information quality has a direct effect on users’ perceived usefulness (0.324***) and perceived ease of use (0.179*). Thus, information quality has a great influence on user experience of mobile library. Users pay attention to the information content which can Constructs 1. Information quality 2. Information environment quality 3. People initiative 4. PU 5. PEU 6. Use attitude 7. Behavioral intention
Model fit indices χ Degrees of freedom χ2/df RMR RMSEA GFI NFI CFI 2
1
2
3
4
5
6
7
0.838 0.268 0.111 0.467 0.252 0.305 0.168
0.797 0.207 0.360 0.294 0.319 0.262
0.820 0.184 0.182 0.200 0.263
0.772 0.500 0.553 0.534
0.748 0.613 0.516
0.729 0.696
0.748
Initial value
Recommended value
203.400 175 1.162 0.033 0.028 0.915 0.900 0.984
– – o2 o0.05 o0.05 W 0.9 W 0.9 W 0.9
Mobile library usage in IoT era
613
Table III. Discriminant validity
Table IV. Goodness-of-fit
LHT 36,4 Hypotheses Relationship
614
H1a H1b H2a
H3a H3b H4 H5 H6 H7 H8
Information quality
4.129 2.099 2.032
*** 0.036 0.042
0.324 0.179 0.158
Supported Supported Supported
2.629
0.009
0.229
Supported
0.811 1.494 4.076 3.411 4.557 5.254 2.017
0.418 0.135 *** *** *** *** 0.044
0.059 0.122 0.360 0.328 0.466 0.200 0.593
Not supported Not supported Supported Supported Supported Supported Supported
Result
0.3
24*
**
Perceived Usefulness 0.3
*
82
58
0*
0.360***
Use Attitude
0.0
59
0.20
*
79*
Information Environment quality
**
0.1
0.1
Behavioral Intention
**
*
0.2
0.593***
29
66
**
0.4
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P
Information quality→PU Information quality→PEU Information environment quality→PU Information environment quality→PEU People initiative→PU People initiative→PEU PEU→PU PU→Use attitude PEU→Use attitude PU→Behavioral intention Use attitude→Behavioral intention
H2b
Table V. Results of the tested hypotheses
CR (t-value)
Standardized structural coefficients
Perceived Ease of Use
Figure 3. Mobile library user behavioral intention factor model
22
People initiative
0.1
Notes: Dashed paths are not supported. *p 97. An examination of the master error logs indicates that database connection errors were the cause for server errors. When compared to the CiteSeerx system which was capable of handling only a limited number of active users (¡ 20), the ad-hoc system has a significantly higher throughput. These results indicate that particular care must be taken in scaling the database, access available when building or deploying CiteSeerx on virtualized infrastructure. Both CiteSeerx and the underlying system must support the expected load.
4.2
Metadata Extraction
We began with a review of the metadata extraction system and its limitations. The metadata extraction system is responsible for processing documents fetched by the crawler and extracting from the documents, metadata crucial in building the database and citation graphs. We examine the existing metadata extraction in detail to understand the process of extracting metadata and limitations in the design and architecture. The process of metadata extraction begins by converting PDF documents into text. This text document is examined by a regular expression based filter to identify scholarly documents. These documents which have passed the filter process are then processed by multiple extraction methods to extract metadata. The extractors include
29 a header parser (27), responsible for extracting the title, authors and citations and a citation parser (32), which extracts the citations, individual fields from the citations and the context of individual citations from the document text. These parsers utilize methods such as Support Vector Machines (33), advanced text segmentation methods (28) for parsing the document to extract the header, and Conditional Random Fields (37) to extraction citation metadata. The modules of the metadata extraction can be operated either in a batch mode or as individual web services coordinated through a Business Process Execution Language (BPEL). The metadata extraction system has several limitations. The processing of documents is a strict pipeline operation, where each process operates in serial and the output of one stage forms the input for the next process in the pipeline. This leads to limited throughput. Parallelizing these processes is not straight forward due to code complexity and limited threading support. Documents to be processed by the extractor have to be located local to the modules. These limitations imply that increasing the resources such as available computation power, memory or speed of components does not aid in improving the throughput of the process.
Fig. 4.5 Current Document Conversion and Extraction Figure 4.6 provides a breakdown for processing 2000 documents (variance: 0.0002, average time for document conversion 0.81, average time for filtering 2.10, average time for extracting headers 8.37, and for extracting citations is 8.88). We notice that the header extraction is the most expensive step, followed by citation extraction, filtering and finally conversion. While some of the documents will convert (14%), not all succeed. This means that the relative throughput of the process is low as a result of time consumed in processing documents which fail the process. The throughput of the process does not increase with increase in resources provided. In an effort to improve the metadata extraction system, we built a metadata extraction system. We begin the process of
30 building a new metadata extraction system by simplifying the process and reducing the complexity of the metadata extraction system.
Fig. 4.6 Current Document Conversion and Extraction
4.3
Extractor Architecture
We propose a new extractor design, which overcomes several of the limitations of the present extractor identified earlier. These include the portability, extensibility of the extractor. Specifically we develop a framework for building SeerSuite extractors. The framework allows users to develop and design their own extractors to recover entities of their interest. The extractor consists of three distinct modules, the document conversion software such as a PDF to text converter, a classification system or an ensemble of classifiers for extracting document metadata and an assembler to aggregate and assemble extracted metadata. The framework makes use of java interfaces as the base for modules allowing the use of dependency injection.
4.4
Reference Implementation
The proposed metadata extraction framework is shown in Figure 4.7, it includes the document conversion subsystem, the header parser, the metadata assembly system and supplements the citation parser in the document acquisition and processing pipeline. The proposed extractor focuses on a select set of metadata; title, author names, section headers, citation contexts and the citations themselves. A crucial improvement introduced in the extractor, is the ability to make use of font and layout information embedded in PDF documents. These features are made available either through interfaces provided by the document conversion application itself (PDFlib TET through tetml) or by modifying the document conversion system as in open source software like PDFBox.
31 Model File Dictionary Content/Features Classifier (First Stage)
PDF to Text (Converter) PDF 1
2 Label/Features
Classifier (Second Stage)
Assembler XML
4
3 Model File
Fig. 4.7 Reference Extractor The supervised machine learning classifier, which processes the output of the document conversion service, by labeling lines is built using an open source library Weka (26). This allows us to adopt different methods, combined with the dependency injection approach followed by the system, to enable different features, entities to be captured to be included with only minor refactoring. The reference system utilizes a random forest classifier, trained on 207 labeled titles, 326 labeled author name(s), 960 labeled section headers, 2397 labeled citations and 1010 labeled contexts for a total of 4090 labeled lines. It utilizes features generated from the content, layout, font information and domain information from dictionaries such as author names (dblp, census), title keywords (dblp), geographical entities, academic entities and stopwords. We were encouraged to use these features such as the font information by results presented in (4) and the use of domain knowledge represented by the dictionaries are discussed in (27). To illustrate the effectiveness of features obtained from the font and layout, we present the results of a ranked features obtained using information gain (1) in 4.1. Relative Position Relative Font size Count of number tokens Count of alpa tokens Count of decimal tokens
Absolute Position X margin Count of ”.” Geometric length Count of Stopwords
Table 4.1 Top features Finally an assembler, which aggregates the labeled entities in the document and links entities such as the citations to the context.
32 4.4.1
Evaluating Quality of Extraction
To illustrate the effectiveness of features obtained from the font and layout, we present the results of ranked features obtained using information gain (1). Since the reference extractor would replace most of the existing extractor we examine the data quality of the extraction. Performance is measured across two criteria, the ability to extract the complete entity and the percentage recovered in case there are several entities of the same time. A less conservative measure could be to allow partial matches, since the inference system in SeerSuite would recover the complete entity from these partially extracted entities. Fields such as title of the document, author names and citations made by the document to other documents are crucial, We manually examined 50 documents for titles, authors and citations recovered. The accuracy of the extraction is compared to that of the existing extraction system. Figure 4.8 shows the relative performance for extracting titles. While the reference implementation has better performance, the overlap in the confidence intervals indicate that there is no significant difference. In case of author extraction shown in Figure 4.9, the performance is once again comparable and there was no significant difference, with the present extractor having a better average performance. In case of the number of authors extracted shown in Figure 4.10, the number of authors recovered was much more than the reference implementation. For extracting citations show in Figure 4.11, the average performance is the same, while the the number of citations recovered is much higher with the reference implementation.
Fig. 4.8 Accuracy in extracting titles Average results for each entity is shown in Table 4.2. From the results, it is clear that the reference metadata extraction improves on the extracting titles, citations, while its performance is marginally worse than the existing extractor for authors. An examination of the errors for the of the reference implementation shows that there were issues with identifying author names containing hypens and
33
Fig. 4.9 Accuracy in extracting author names Extractor Reference Present
Titles 84% 80%
Authors (Recovered) 88% (86.05%) 90% (93.70%)
Citations (Recovered) 98% (87.90%) 98% (79.99%)
Table 4.2 Quality of extraction unicode characters, and certain entities such as the university name, department were labeled as author names.
4.5
Summary
We discussed two major challenges in migrating SeerSuite to the cloud and our approach to overcoming these obstacles. We presented an abstraction for the repository services and an implementation of a metadata extractor. The results from our evaluation of the repository and the metadata extractor show that the repository can be abstracted successfully and extends both the performance and features of the existing system. The metadata extractor improves on the complexity of the system and quality of extraction.
34
Fig. 4.10 Recovery of author names
Fig. 4.11 Recovery of citations
35
Chapter 5
Scaling SeerSuite
We addressed the improvements to the metadata extractor code base to simplify and reduce the code complexity of the metadata extractor, while these changes are significant, issues with low throughput of the extractor are still a significant concern. At the time of writing this document, several hundred thousand documents had yet to be processed by the extractor at CiteSeerx . Increase in the resources allocated to the existing extractor do not lead to an increase in the throughput, we take advantage of the simplified architecture, and code base to take advantage of distributed computing approaches to scale the metadata extractor.
5.1
Stand alone performance
We present the evaluation of the existing and proposed extractor to illustrate the need for scaling the metadata extractor. To evaluate the performance of the extractor in terms of throughput, we make use of a collection of documents obtained from the CiteSeerx collection and the web. Both extractors are run in batch mode on the existing processing infrastructure 1 . We examine the average time taken for processing a document on the existing infrastructure, through the average number of wall clock time taken for converting a set of 2,498 documents. The documents had an average size of 700 Kilobytes and 97% of the documents were smaller than 2 Megabytes in size. In this case the existing extractor consumed 3.32 seconds on average to process a single document, compared to the 3.20 seconds consumed by the proposed extractor. Thus there has been a marginal improvement (4%) in the time taken on average. 5.1.1
Stand alone performance in the cloud
To evaluate the operation of the new metadata extractor in a cloud environment, we hosted the extractor along with the necessary libraries, dictionaries and files to process documents in different cloud computing environments such as the Amazon EC2 and Windows Azure. In this case the performance of the system in the time taken to process a document was examined. The extractor processed 120 documents (97% less than 2 Megabytes) all located in the local storage/filesystem for this evaluation. The output of the extractor (XML) was also stored on the local file system. There were no changes made to the application 1
Dual core processor, 3 GB of RAM, TB of disk space
36 code, except for changes to the packaging according to the cloud infrastructure to which the extractor would be deployed. In case of Amazon EC2 2 , the application was shipped into the instance as a single zip file along with a copy of java, pdfs and models, while in the case of Windows Azure 3 , we made use of available plugins to package java, pdfs and models. A local system similar to the cloud instances 4 was utilized for this evaluation. Results are shown in table 5.1 Infrastructure Local Amazon EC2 Azure
Average Time (sec/doc) 1.55 2.25 3.6
Table 5.1 Performance in the Cloud Amazon EC2 instance provides the closest performance to the local deployment, while the performance on Azure indicates that there maybe optimizations to be made to improve performance.
5.2
Goals
There are several approaches to scaling systems and in particular information systems in the cloud. To better identify the most appropriate choice, we study constraints placed on SeerSuite components due to the overall design goals of SeerSuite. These are closely identified with the requirements for SeerSuite itself. 5.2.1
Management
The most effective approach to management of a component or process would be to allow the system to function in an automatic manner, with manual intervention required only in anomalous situations. 5.2.2
Flexibility
There is a constant need for adding new features and improving the existing features. In the present scenario, components such as the metadata extractor would need to undergo large scale modifications to support new features. 5.2.3
Heterogeneous Environment and Portability
One of the main requirements for SeerSuite is the hosting of its components in environments with various operating systems, storage and access. 2
Medium Extra Large: 4 CPU (8 ECU), 15 GB RAM, 8 GB Storage Extra Large: 8 Cores, 14 GB, 2 GB Storage 4 8 Cores, 16 GB RAM, 1 TB Storage 3
37 5.2.4
Reliability
Reliability of the component and its ability to function in case of failures within the component or in the supporting components is crucial, to maintain uninterrupted service to users. 5.2.5
Discussion
With these goals in context, we also identify constraints placed on the metadata extraction system due to its design. Among these is the number of model files and dictionaries being utilized which would need to be distributed along with the extractor. The input to the extractor is a large number of small files, in the PDF/PS format. These requirements and constraints mean that MapReduce implementations such as the Hadoop framework cannot be utilized for metadata processing. The requirement for reliability and portability mean that the system cannot be based on scale up, but may also need to take advantage of scaling out. This discussion thus guides us to the use of message oriented middleware.
5.3
Message Oriented Middleware and Extraction
While the issues related to flexibility and portability are addressed with the use of the new extractor, the issue of scalability still needs to be addressed. The performance of the system has been marginally improved, however the throughput of the system remains constrained with the same limitations of the existing extractor. To address these issues, we make use of a message oriented middleware system, utilizing a publish subscribe pattern. Message oriented middleware (3; 11) offers several advantages in building distributed systems involving heterogeneous components, networks and infrastructure. We were influenced by the use of grid based service oriented middleware in WSPeer (29) for message exchanges, Meghdoot (24) for content based publish/subscribe in Peer to Peer environments, of particular interest is the discussion on software engineering and middleware presented in (13). Issues related to scalability and heterogeneity discussed in the paper have been addressed in our work by utilizing queue servers with the properties of replication, persistence and availability in AMQP based queue servers and using popular formats such as JSON to marshal messages. Message oriented middleware is already part of SeerSuite and CiteSeerx deployment,a pre-existing messaging framework for interaction between components of the crawler and a legacy messaging system for interacting with the crawler through JMS already exists as part of SeerSuite. Message oriented middleware has been utilized for applications as diverse as cyberinfrastructure in TeraGrid (48), sensor networks (49) and supply chain management (8). In addition the publish/subscribe (14) model allows for loose coupling and scalability which are suited to the goals identified for SeerSuite. Addition of new metadata extractors (41; 5) as subscribers, grouping extractors is possible without modification to the existing pipeline.
38 5.3.1
Wrapper
The proposed metadata extraction service by itself offers limited ability to interact with a queue or other interfaces. We design a wrapper which provides the metadata extractor with methods to interact with interfaces. The wrapper design does not limit the operations of the extractor in any manner, the extractor could be utilized in the same fashion as the existing extractor. 5.3.1.1
REST Module
The wrapper provides common REST API functionality to the extractor. While the GET method in many cases could already be part of the pdf converter, a POST method for uploading processed documents is included in the wrapper. 5.3.1.2
Subscriber and Queue Client
To interact with queue’s the wrapper includes client functionality for AMQP and Amazon SQS. These interfaces allow the extractor to be a consumer for queues.
5.4
Workflow and Deployment with Wrapper
The use of the wrapper enables the metadata extraction system to interact with message oriented middleware A typical deployment is shown in figure 5.1. Extraction Queue
Ingestion Queue Extractor
Crawler 3 1
6
4
7
2 5 8
Web
Virtual Store/S3
Ingestion
Fig. 5.1 Deployment Architecture The workflow of the process proceeds as follows. The crawler on a successful identification and fetching of a document from the web (steps 1 and 2), posts a message to the extractor queue (step 3). This message either points to the URL of the document or the URL at the virtual storage. The extractor then consumes messages (step 4) from the queue and processes the document (step 5), generating an ingestable document on success. A message of completion (step 6) is posted onto the ingestion queue, which then handled by the ingestion system (steps 7 and 8).
39 5.4.1
Deployment with Wrapper
For the purpose of evaluation, we deployed the extractor with the wrapper across physical systems, with a single AMQP 5 queue server hosting a topic exchange. The message template for the deployment is described in 5.2. The message format provides flexibility and allows the message to identify the output location for a document. This is particularly relevant as the extractor can be deployed in different environments, which include deployments on physical servers, computations on physical servers with virtual/cloud storage, cloud deployment with physical storage, cloud deployment with cloud storage and combinations of physical and cloud based deployment with local and cloud based storage. The message includes, a description of the resource to be processed in the form of an URL or location in the physical storage. The destination in a similar fashion could represent either a local file location or a virtual/cloud storage location. The destination and destination type allow us to use virtual/cloud storage and different service oriented approaches (WSDL,REST) to interacting with the storage. In our deployment, we serialized the message submitted to the queue as a JSON string. ID URL (input) Destination Destination End Point (output) Destination Type Table 5.2 Message Template at the Extractor Queue
5.4.2
Shared Storage
The motivation for operating the metadata extractor in this mode is the presence of a shared storage system such as cluster storage or network storage device. The messaging system in this case is used to coordinate various extractors and serves as a buffer between the crawler, metadata extraction system and other components in the pipeline (Figure 5.2). By utilizing the message template discussed earlier, we can select the extractor output location at the crawler. This mode does not take advantage of most features of message oriented middleware other than the coordination between independent extractors, which could be accomplished using other mechanisms. The performance in this case is also marginally worse than that of the standalone extractor without the wrapper system in place. Operational limitations restrict the extractor to processing sets which can be comfortably hosted locally or on the shared storage. The table 5.3 shows the performance of the metadata extractor in this mode of operation, both when compute and storage are in the cloud host (extra large instance) 5
http://www.amqp.org/,http://www.rabbitmq.com/
40 Extraction Queue Crawler
Extractor
Shared Storage
Web
Fig. 5.2 Local or Shared Storage or hosted on physical hardware/operating system with other SeerSuite components such as the crawler. The AMQP queue server was hosted on physical systems. During the evaluation, we emulated a crawler by a simple client, generating crawl messages of with different input, output locations on demand. Scenario Local Cloud
Input Location Same Host Same Host
Computation Physical Cloud
Output Storage Same Host Same Host
Avg. Time (s) 1.57 2.28
Table 5.3 Local Storage
5.5
Processing with Message Oriented Middleware
We consider the case of deploying the extractor in an distributed environment, where extractors make use of a virtual storage device for processing incoming documents. The virtual storage server (discussed in the previous chapter) in this context provides methods such as GET, PUT, DELETE for storing documents using a REST interface. A more detailed discussion of the virtual storage system has been presented in (52). The use of a virtual storage system is enabled by features available as part of the wrapper. The documents to be processed are fetched from the virtual storage, once the document is processed it is again stored with the output in the virtual storage. This enables many extractors to function at the same time without the need for a shared storage system. This also frees the extractor from being needs to be modified. With these we can examine the various scenarios for operating the metadata extractor.
41
5.6
Scenarios of Operation
To take advantage of the features introduced with the virtual storage, middleware and multiple extractors, We consider deployment scenarios with multiple extractors working in parallel. This is done by distributing the extractors across several systems both physical servers and in the cloud as shown in figure 5.3 and 5.4. The systems can also be distributed by locating one instance on a physical server and another in the cloud, resulting in a hybrid deployment. Extraction Queue Message Crawler Message Extractor REST
REST Extractor Virtual Storage
Web Physical Servers/Cloud Instances
Fig. 5.3 Operating Modes (a) Cloud or Physical
5.6.1
Existing physical infrastructure
In this scenario, the extractor instances are located on physical servers. An AMQP server and virtual storage are utilized. This mode allows us to take advantage of existing infrastructure, without recourse to deploying instances in the cloud. 5.6.2
Cloud infrastructure
As the needs of extraction grows beyond the capacity of our present infrastructure, cloud based instances can be utilized. In this case, we host the virtual storage and queue locally and the extractor instances are hosted in the cloud. 5.6.3
Hybrid infrastructure
A combination of instances hosted in the cloud and locally on physical servers, serves the need of dynamic workloads. In this scenario, while a instance can be maintained in physical servers, instances can be initialized in the cloud on demand. This allows the extraction system to meet dynamic workloads as a result of crawl or submission, more efficiently. This mode of operation is enabled by using a local queue server, virtual storage.
42 Extraction Queue Message
Crawler
Cloud Instance
Message Extractor REST
Virtual Storage Web
Extractor REST Physical Server
Fig. 5.4 Operating Modes (b) Hybrid 5.6.4
Evaluation
With the infrastructure scenarios for physical, cloud and hybrid infrastructure in mind, we evaluate the performance of the extractor system illustrated in Table 5.4 (T/Doc represents time consumed per document). In case SQS were used, we made use the message visibility feature to avoid processing of the same message by multiple extractors (by marking a message being consumed by an extractor as hidden). Scenario Local Cloud Hybrid
Hosts 2 2 2
Input Host Web Host Web Host Web Host
Compute Local Cloud Local, Cloud
Storage Virtual Virtual Virtual
T/Doc (s) 10.53 14.58 9.96
Table 5.4 Average time consumption in various modes of operation From the evaluation the operation of the hybrid service has the best performance of the combinations. This is marginally ( 5%) better than the distribution over physical servers. The cloud based instances have the worst performance which can be explained by network latencies. It is also clear from the evaluation that the overall performance of the system is much worse than the performance of the standalone mode or existing extractors. This leads us to examine aspects of the extractor which we can take advantage of for further improvements in performance.
5.7
Threading
From the evaluation of the scenarios of operation, we realize that the many of the issues related to performance are linked to I/O operations, shared objects, the network and related issues. In the scenarios we have examined, the individual extractors operated
43 as independent processes. We can take advantage of threading in the application, possible with the new extractor to boost performance. With this in focus, we examined the utilizing a thread pool pattern for the extractor. The use of thread pools is guided by the fact that while the extractor has been simplified, it still has a large memory footprint (due to classifiers and model files). We limit the number of active threads by the use of a thread pool. We evaluate the performance of the system with threading in the same scenarios. Results are showing in Table 5.5 (T/Doc represents time consumed per document) and Figure 5.5. Scenario
Hosts
Input Host
Compute
Storage
Local Cloud Hybrid
2 2 2
Web Host Web Host Web Host
Local Cloud Local, Cloud
Virtual Virtual Virtual
T/Doc (s) 0.67 0.59 0.30
Table 5.5 Average time consumption in various scenarios with threading
Fig. 5.5 Performance in different scenarios It is apparent that there has been tremendous improvement in the operation of the extraction performance when threading is used along with scale out. The wide distribution of the throughput also points to issues with network issues. We examine the scaling capabilities of the metadata extractor with different number of instances to identify improvements in performance. We note that the performance of the distributed system for extraction in Table 5.6 and Figure 5.6 scales well with increase in the number of instances.
44 Instances/Systems 2 4 8 16
Time/Document (s) 0.67 0.34 0.26 0.12
Table 5.6 Average time consumed utilizing multiple instances
Fig. 5.6 Scalability across instances
5.8
Lessons Learned
We outline some of the several lessons learned in our experience in scaling the metadata extraction system. These focus on the designing a scalable system, reliability of the system. 5.8.1
Scaling
While the choice of threading seems obvious, the approach to threading in the case of metadata extraction needs to be considered carefully. In our implementation of the reference metadata extraction system we utilize model, dictionary files. The threading process has to pre load the model files into shared variables to avoid the penalty of loading these files in each thread. A naive implementation of threading will not result in any improvement in throughput. An approach to threading the extractor has to consider the individual modules and their dependencies (model used) within the application and on external entities (models) to be successful. 5.8.2
Reliability
The use of third party libraries, the processing of documents obtained from the web can result in the application becoming unstable due to failures in the libraries or while processing malformed or corrupt documents, suitable exception handling methods
45 are essential. In addition by distributing the workload across several systems can allow us to process documents, even when one of the application instances is blocked or failed. This supports the use of scale out along with scaling up for improving throughput, while maintaining reliable and robust operations.
5.9
Summary
We identified the need for scaling the metadata extractor by evaluating its performance in a stand alone mode. We examined the constraints and goals placed on the design of the distributed extraction system. With these goals and constraints in context, we proposed a message oriented middleware system for distributing extraction. We evaluated the performance of the system across local and cloud infrastructure. By utilizing threading, we were able to improve the performance of the system significantly. We also discuss some of the most important lessons learned during the design and implementation of the extraction system.
46
Chapter 6
Conclusions
We presented the architecture and design of SeerSuite, an open source digital library framework. SeerSuite, its components and its workflow were discussed in detail. SeerSuite by itself is an improvement on CiteSeer and progress towards building a reliable, robust information retrieval system. Its attributes such as loose coupling of components, service oriented architecture, state of art machine learning methods enable researchers and users in building information retrieval systems including the whole or components of SeerSuite. As such, the lessons learned in building SeerSuite are valuable far beyond its instances. Our experiences with SeerSuite and an understanding of the limitations of SeerSuite motivated us to examine infrastructure and components whose design would extend scalability of SeerSuite instances. Features of such as reduced maintenance requirements while providing the ability to handle dynamic workloads, the pay as you go model make cloud infrastructure attractive to hosting instances of SeerSuite. We studied the feasibility of hosting SeerSuite instances in the cloud by profiling the components and identifying the costs, effort required for such a migration. While the cost of hosting an instance in the cloud was prohibitive, we show that hosting instances of SeerSuite during peak load conditions, partitioning content across application and data components can be useful for taking advantage of cloud infrastructure. Instances of SeerSuite are limited by the existing repository architecture. Building large scale collections requires expensive storage subsystems. To overcome this challenge, we virtualized the repository storage system, distributing the storage across systems and utilizing a REST interface to interact with other components of SeerSuite. We evaluated this storage system and found that it improves on the services provided by the present system. The metadata extraction system suffers due to its design and code complexity limiting the extensibility, portability and scalability of the entire instance. We designed and developed a metadata extraction framework, and a reference implementation to address these issues. The quality of the metadata extracted is comparable to the existing system, while the complexity of the code, dependencies have been greatly reduced. While we addressed the extensibility and portability of the metadata extraction system, the question of scaling the metadata extraction system to meet user needs to be addressed. We discuss the constraints and proposed a message oriented middleware, publish subscribe topic exchange approach to scaling out the metadata extraction system. We significantly improve the throughput of the system by using threading. Our contributions to understanding SeerSuite, its workflow. The study of migrating SeerSuite instances to the cloud, addressing limitations in the repository and
47 metadata extraction system should greatly enhance the scalability, extensibility of SeerSuite instances.
48
Bibliography
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50 [27] Han, H., Giles, C. L., Manavoglu, E., Zha, H., Zhang, Z., and Fox, E. A. Automatic document metadata extraction using support vector machines. In JCDL ’03: Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries (2003), pp. 37–48. [28] Han, H., Manavoglu, E., Zha, H., Tsioutsiouliklis, K., Giles, C. L., and Zhang, X. Rule-based word clustering for document metadata extraction. In SAC (2005), pp. 1049–1053. [29] Harrison, A., and Taylor, I. Service-oriented middleware for hybrid environments. In Proceedings of the 1st international workshop on Advanced data processing in ubiquitous computing (ADPUC 2006) (New York, NY, USA, 2006), ADPUC ’06, ACM, pp. 2–. [30] Hongjun, Y. Innovation of digital reference service model in the cloud computing environment [j]. Information Studies: Theory & Application 1 (2010). [31] Huang, J., Ertekin, S., and Giles, C. L. Efficient name disambiguation for large scale databases. In The 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (2006), pp. 536–544. [32] Isaac Councill, C. L. G., and Kan, M.-Y. Parscit: an open-source crf reference string parsing package. In Proceedings of the Sixth International Language Resources and Evaluation (LREC’08) (Marrakech, 2008), European Language Resources Association. [33] Joachims, T. Text categorization with support vector machines: Learning with many relevant features. Machine Learning: ECML-98 (1998), 137–142. [34] Kahn, R., and Wilensky, R. A framework for distributed digital object services. International Journal on Digital Libraries 6, 2 (2006), 115–123. [35] Kataria, S., Mitra, P., and Bhatia, S. Utilizing context in generative bayesian models for linked corpus. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (2010). [36] Khajeh-Hosseini, A., Sommerville, I., Bogaerts, J., and Teregowda, P. Decision support tools for cloud migration in the enterprise. In Cloud Computing (CLOUD), 2011 IEEE International Conference on (2011), IEEE, pp. 541–548. [37] Lafferty, J., McCallum, A., and Pereira, F. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. [38] Li, H., Councill, I., Lee, W.-C., and Giles, C. L. Citeseerx: an architecture and web service design for an academic document search engine. Poster Session 15th International World Wide Web Conference (2006). [39] Li, H., Lee, W., Sivasubramaniam, A., and Giles, C. Workload analysis for scientific literature digital libraries. International Journal on Digital Libraries 9, 2 (2008), 139–149.
51 [40] Liu, Y., Bai, K., Mitra, P., and Giles, C. L. Tableseer: automatic table metadata extraction and searching in digital libraries. In JCDL (2007), pp. 91–100. [41] Liu, Y., Mitra, P., Giles, C., and Bai, K. Automatic extraction of table metadata from digital documents. In Proceedings of the 6th ACM/IEEE-CS joint conference on Digital libraries (2006), ACM, pp. 339–340. [42] Mika, P., and Tummarello, G. Web semantics in the clouds. IEEE Intelligent Systems (2008), 82–87. [43] Mitra, P., Giles, C. L., Sun, B., and Liu, Y. Chemxseer: a digital library and data repository for chemical kinetics. In CIMS ’07: Proceedings of the ACM first workshop on CyberInfrastructure: Information Management in eScience (2007), pp. 7–10. [44] Nurmi, D., Wolski, R., Grzegorczyk, C., Obertelli, G., Soman, S., Youseff, L., and Zagorodnov, D. The eucalyptus open-source cloud-computing system. In Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid-Volume 00 (2009), IEEE Computer Society, pp. 124– 131. [45] Pinto, H., Staab, S., Tempich, C., and Sure, C. Y. Semantic web and peerto-peer. ¨ szo ¨ rmenyi, L. A survey of web cache replacement strate[46] Podlipnig, S., and Bo gies. ACM Computing Surveys (CSUR) 35, 4 (2003), 374–398. [47] Singh, A., Srivatsa, M., and Liu, L. Search-as-a-service: Outsourced search over outsourced storage. ACM Trans. Web 3, 4 (2009), 1–33. [48] Smith, W. An information architecture based on publish/subscribe messaging. In Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery (2011), ACM, p. 27. ˜ es, G., Vasconcelos, G., Vieira, M., Rosa, N. S., [49] Souto, E., Guimara and Ferraz, C. A. G. A message-oriented middleware for sensor networks. In Middleware for Pervasive and Ad-hoc Computing (2004), pp. 127–134. [50] Tan, Q., Mitra, P., and Giles, C. Metadata extraction and indexing for map search in web documents. In Proceeding of the 17th ACM CIKM (2008), pp. 1367– 1368. [51] Teregowda, P., Urgaonkar, B., and Giles, C. L. Citeseerx : A cloud perspective. HotCloud 2010, 2nd USENIX Workshop on Hot Topics in Cloud Computing (2010). [52] Teregowda, P., Urgaonkar, B., and Giles, C. L. Cloud computing: A digital libraries perspective. Cloud Computing, IEEE International Conference on 0 (2010), 115–122.
52 [53] Teregowda, P. B., Councill, I. G., Fernandez, J. P. R., Kasbha, M., Zheng, S., and Giles, L. C. Seersuite: Developing a scalable and reliable application framework for building digital libraries by crawling the web. In USENIX Conference on Web Application Development (2010). [54] Treeratpituk, P., and Giles, C. Disambiguating authors in academic publications using random forests. In Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries (2009), ACM, pp. 39–48. [55] Vinoski, S. Rest eye for the soa guy. IEEE Internet Computing (2007), 82–84. [56] Walker, E., Brisken, W., and Romney, J. To lease or not to lease from storage clouds. Computer 43 (2010), 44–50. [57] Wood, T., Cecchet, E., Ramakrishnany, K., Shenoy, P., van der Merwey, J., and Venkataramani, A. Disaster recovery as a cloud service: Economic benefits & deployment challenges. In 2nd USENIX Workshop on Hot Topics in Cloud Computing (2010). [58] Zhu, T., Gandhi, A., Harchol-Balter, M., and Kozuch, M. Saving cash by using less cache. In USENIX Workshop on Hot Topics in Cloud Computing (HotCloud) (2012).
Vita Pradeep Teregowda Education The Pennsylvania State University University Park, Pennsylvania 2008–Present Ph.D. in Computer Science and Engineering, expected in December 2012 The Pennsylvania State University University Park, PA Masters in Electrical Engineering and High Performance Computing
2001–2004
University of Mysore Mysore, India Bachelors in Electrical and Electronics Engineering
1996–2000
Research Experience Doctoral Research The Pennsylvania State University 2008–Present Dissertation Advisor: Prof. C. Lee Giles Graduate Research The Pennsylvania State University 2001–2004 Research Advisor(s): Prof. Constantino Lagoa, Prof. C. Lee Giles, Nirmal Pal, Prof. Arvind Rangaswamy Undergraduate Research Mysore University 1999–2000 Research Advisor: A. S. Arvindamurthy
Publications Pradeep Teregowda, Madian Khabsa, Clyde Giles, A System for Indexing Tables, Algorithms and Figures. JCDL 2012. Jian Wu, C. Lee Giles, Pradeep Teregowda, Juan Pablo Fernndez Ramrez and Prasenjit Mitra. A Study of the Crawling Strategy Evolution for Academic Document Search Engines. WebSci 2012. Ali Khajeh-Hosseini, Ian Sommerville, Jurgen Bogaerts, Pradeep B. Teregowda, Decision Support Tools for Cloud Migration in the Enterprise. IEEE CLOUD 2011. Pradeep B. Teregowda, Bhuvan Urgaonkar, C. Lee Giles: Cloud Computing: A Digital Libraries Perspective. IEEE CLOUD 2010. Pradeep B. Teregowda, Isaac G. Councill, Juan Pablo Fernndez Ramrez, Madian Khabsa, Shuyi Zheng, C. Lee Giles. USENIX WebApps 2012. Pradeep Teregowda, Bhuvan Urgaonkar, C. Lee Giles, CiteSeerx: A Cloud Perspective, USENIX HotCloud 2010. Pucktada Treeratpituk, Pradeep Teregowda, Jian Huang, and C. Lee Giles, SEERLAB: A System for Extracting Keyphrases from Scholarly Documents, SemEval 2010. Yves Petinot, C. Lee Giles, Vivek Bhatnagar, Pradeep B. Teregowda, Hui Han, Isaac G. Councill, CiteSeer-API: towards seamless resource location and interlinking for digital libraries, CIKM 2004. Yves Petinot, C. Lee Giles, Vivek Bhatnagar, Pradeep B. Teregowda, Hui Han, Isaac G. Councill, A service-oriented architecture for digital libraries, ICSOC 2004. Yves Petinot, C. Lee Giles, Vivek Bhatnagar, Pradeep B. Teregowda, Hui Han, Enabling interoperability for autonomous digital libraries: an API to citeseer services, JCDL 2004. Yves Petinot, Pradeep B. Teregowda, Hui Han, C. Lee Giles, Steve Lawrence, Arvind Rangaswamy, Nirmal Pal, eBizSearch: An OAI-Compliant Digital Library for eBusiness, JCDL 2003. C. Lee Giles, Yves Petinot, Pradeep B. Teregowda, Hui Han, Steve Lawrence, Arvind Rangaswamy, Nirmal Pal, eBizSearch: a niche search engine for e-business, SIGIR 2003.
DIGITAL ERA: UTILIZE OF CLOUD COMPUTING TECHNOLOGY IN DIGITAL LIBRARY
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DIGITAL ERA: UTILIZE OF CLOUD COMPUTING TECHNOLOGY IN DIGITAL LIBRARY T. RAGHUNADHA REDDY Librarian, SVCET, Chittoor.
Abstract With the purpose of applying cloud computing to digital library, the paper initially describes cloud computing and analyzes current status of cloud computing in digital library. Then it proposes the architecture of cloud computing in digital library and summarises the application of cloud computing in digital library. Finally the author brings out the future improvement in digital library using cloud computing technology.
Keywords: Information resources, Digital Library, Server. INTRODUCTION Within the past decade the number and kinds of digital library information sources have proliferated Computing system advances and the continuing networking and communication revolution have resulted in a remarkable expansion in the ability to generate process and disseminate digital information. Together, these developments have made new forms of knowledge1 repositories and information delivery mechanisms feasible and economical. Digital library, as we all know, is famous for its academic and technical influences. And IT technology has been the driving force of library development. What's more, librarians can keep using new technology to develop digital library and optimize library service. With the expansion of Cloud Computing application, this paper proposed to apply Cloud Computing in digital libraries. By establishing a pubic cloud among many digital libraries, it not only can conserve library resources but also can improve its user satisfaction. Cloud Computing is a completely new Information Technology and it is known as the third revolution after PC and Internet in IT. As it is still an 92 | II JJ O OD D LL SS
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evolving paradigm, its definitions, use cases, underlying technologies, issues, risks, and benefits will be refined in a spirited debate by the public and private sectors. According to the definition of NIST (National Institute of Standards and Technology), Cloud Computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. To be more specific, Cloud Computing is the improvement of Distributed Computing, Parallel Computing, Grid Computing2 and Distributed Databases. What is Cloud Computing “A Cloud is a type of parallel and distributed system consisting of a collection of inter-connected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resource(s) based on service-level agreements established through negotiation3 between the service provider and consumers.” Figure1 denote resources of cloud computing. Figure1: Resources of Cloud Computing Technology
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Cloud computing seems to offer some incredible benefits for communicators: the availability of an incredible array of software applications, access to lightning-quick processing power, unlimited storage4, and the ability to easily share and process information. All of this is available through your browser any time you can access the Internet. It means computing ability also can be a kind of commodity, as gas, water and electric, easy to use and cheap cost. “Could Computing” brings such a change – “computer storage computing center” are set up by professional network companies such as Google and IBM, through one cable and user can access easily with browser, make “Could” as the center of material storage and application services. Definition Cloud Computing is associated with a new paradigm for the provision of computing infrastructure. This paradigm shifts the location of this infrastructure to the network to reduce the costs associated with the management5of hardware and software resources. The Cloud is drawing the attention from the Information and Communication Technology (ICT) community. “The key concept behind the Cloud6 is Web application... a more developed and reliable Cloud. Many find it’s now cheaper to migrate to the Web Cloud than invest in their own server farm ... it is a desktop for people without a computer”. R. Bragg (2008) Need for Cloud Computing in Digital Libraries Digital library, as a most important academic and scientific research base, charges for providing information services for its users. In the past, most libraries insisted that their service is based on their own library resources. So librarians scarcely considered users' demands. But today, digital libraries have changed this viewpoint. And librarians usually need to collect as more information as they can according to users' requirements. Then they will analyze 94 | II JJ O OD D LL SS
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the information and sort out them. Finally, they will provide them for users in some certain technical methods. However, services in digital libraries will increasingly focus on users' demanding in future. And the ultimate goal of digital library is to offer appropriate, comprehensive and multi-level services for its users. With the introduction of Cloud Computing7 to digital library, services of libraries will have a new leap in the near future. Services provided by digital libraries will become more user-centric, more professional and more effective, etc. And we all believe that digital libraries will create more knowledge benefits for our country with the help of Cloud Computing. The State of Cloud Computing in Digital Library Cloud platforms enable organizations to use external expertise and resources to deliver complex services, remove the need for organizations to invest in server infrastructure, and lower the cost for organizations seeking elastic computing resources. Libraries have been adopting cloud-based solutions for different services including electronic journal access management, statistics tracking, digital library hosting, and even integrated library system (ILS) hosting. This has allowed libraries to make strategic choices about the allocation of resources and to offer better service than would be possible if relying on inhouse solutions. While much of the focus on cloud computing in digital libraries has been on subscription service or platform (e.g. ILS hosting). There are cases where digital libraries need computing resources for requirements that are not provided by service or platform providers. Cloud computing can be divided into five categories: Communication-as-a-Service (CaaS), Infrastructure-as-aService (IaaS), Monitoring-as-a-Service (MaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS). Figure 2 represented below depict the types of cloud computing services and its related features.
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Figure2: Types of Cloud Computing Services
IaaS CaaS
MaaS
Types of Cloud Computing Services
PaaS
SaaS
Communication as a Service (CaaS): Allow for certain messaging8 tools viz voice over IP (VOIP), Instant Messages (IM) and Video Conferencing. Information as a Service (IaaS): Allows customer to maintain owner and management of their application while off-loading infrastructure management9 to the Iaas provider. Monitoring as a Service (MaaS): Outsourcing of security service10 to a third party security team. Platform as a Service (PaaS): Meant for web-based11 development infrastructure. Software as a Service(SaaS):When a software vendor supplies software12 over a network as opposed to the typical distribution of installation of individual computers
Digital libraries have quietly been on the forefront of cloud computing technology for a number of years. The use of SaaS in digital libraries reaches back into early 2000 with the establishment of companies like Serials Solutions13. Much of the work is migrating to electronic journals and it has focused on a SaaS platform, and recent companies14 such as Lib Guides have 96 | II JJ O OD D LL SS
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shown that libraries are willing to invest in SaaS solutions. In the IaaS arena, Amazon Elastic Computing Cloud (EC2) offers IT infrastructure for organizations to launch different sized servers using a variety of operating systems, including several flavors of Linux and Windows. EC2 provides organizations with essentially unlimited storage using their S3 service, the ability to take snapshots of both data and servers, and the ability to include EC2 servers in an organization’s private15 network. Wheeler and Waggener (2009) use this classification16 (SaaS, PaaS, and IaaS) as a launching pad to discuss ways in which they can be used to enable collaboration or ’sourcing’ between institutions and consortia. Marshall Breeding (2009) places these three types of services17 within the context of other infrastructure and hosting options such as co-location (the duplication of specific IT resources in multiple places), shared and dedicated hosting (licensing a shared or distinct portion of a server for use), and cloud computing (abstracting the hardware, software, and service layers to provide an extensible computing environment). Embedded within these classifications are needs and use arguments, organizational goals, and institutional priorities. Digital libraries are in a unique position to experiment with cloud computing given their serviceoriented mission and need to find appropriate solutions using limited resources. Fox (2009) observes that the goals of the organization18 have an impact on their use of cloud solutions (2009). Digital Libraries are often supported by external or organization level IT services and do not have internal expertise on advanced IT management. Many libraries have been active in investigating innovative uses of cloud computing (Kroski, 2009), including new ways of using infrastructure19 services. Kroski’s article mentions the use of Amazon EC2 services by both the DC Public Library system and Ohio Link to provide library IT services using IaaS techniques. Both the Gartner Hype report20 on cloud computing (2009) and the Educause Horizon report21 (2009) point to the expansion of cloud services in the coming years. Digital libraries and especially academic organizations have largely followed suit, having already migrated key services such as Open URL providers, and federated and pre-indexed search engines.
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Architecture of Cloud Computing in Digital Library The architecture22 behind cloud computing is a massive network of "cloud servers" interconnected as if in a grid running in parallel, sometimes using the technique of virtualization to maximize computing power per server. The following figure 3 represents the architecture of cloud computing in digital library. Figure3: Cloud Computing Architecture for Digital Library
A front-end interface allows a user to select a service from a catalogue. This request gets passed to the system management which finds the correct resources, and then calls the provisioning services which carves out resources in the cloud. The provisioning service may deploy the requested stack or web application as well.
User interaction interface: This is how users of the cloud interface with the cloud to request services.
Services catalogue: This is the list of services that a user can request.
System management: This is the piece which manages the computer resources available. 98 | II JJ O OD D LL SS
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Provisioning tool: This tool carves out the systems from the cloud to deliver on the requested service. It may also deploy the required images.
Monitoring and metering: This optional piece tracks the usage of the cloud so the resources used can be attributed to a certain user.
Servers: The servers are managed by the system management tool. They can be either virtual or real.
Application of Cloud Computing in Digital Library Digital library is a development-oriented hardware and software integration platform, through technical and the product integration. Each kind of carrier digitization carries on the effective deposit and the organization provides the network with effective service. Figure 4 illustrates application of cloud computing in digital library. Figure4: Application of cloud computing in digital library
Cloud computing offers real alternatives to Information Technology field for improved flexibility and lower cost. Digital Libraries23 are developing for software applications, platforms, and infrastructure as a service to Information Technology departments over the “cloud”. It also provides for better and easier management of data security, since all the data is located on a central server, so 99 | II JJ O OD D LL SS
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administrators can control who has and doesn’t have access to the files. The main objective of cloud computing is to use a specific software through calculation and the data stored in a desired computer distribution which causes the enterprise to reduce cost and improve performance. Digital library represents one kind of new infrastructure and the environment; through cloud computing technology since it uses resources more effectively and can solve the constraints in digital library. Cloud Computing Recognition Permissions Apprehension I.
Cloud Computing Recognition: Using cloud computing technology, one can share the server in many application procedures, realize the resource sharing thus reduce server’s quantity, minimize the cost. Therefore implementation of cloud computing technology in digital library will promote user’s work and study to get done with a greater efficiency. Every cloud computing server may function alike computer server and save the server or the broad band resources and so on. Figure 5 represents implementation of cloud computing technology in digital library where every cloud represents any digital library database resource, every two clouds or more clouds may compose a bigger cloud and might divide the cloud or network of clouds by different section.
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Figure5: Implementation of Cloud Computing Technology in Digital Library
User 1
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A SaaS and PaaS service typically includes a database, middleware and developmental tools, which are all in the form of services through the internet, which facilitate the users and suppliers to reduce cost. IaaS provide servers, storage and networking hardware. SDK, Software Development Kit, refers to supporting development of a specific software, documentation, samples and collection of tools. In general SDK is used under windows platform. II. Permission Apprehension: Cloud environment is a highly developed network environment; it appears to the users of high-quality service and high security. The Cloud computing techniques and methods applied to digital libraries, not only can improve the utilization rate of resources to address the imbalance in development between regions, but also can make more extensive use of cloud computing to our work life. Figure 6 denotes permission apprehension of cloud computing in digital library.
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Figure6: Permission Apprehension of Cloud Computing in Digital Library Cloud Platform
Definition / Update Key
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Initially user is requested to internet transmission, and between the cloud platform and continuous Internet revision key, in order to protect the platform. Simultaneously the cloud platform defines an access rule to its users and the user transmits their own status to the platform and the platform grants the user specifically for the permissions statement. Future Improvements in Digital Library Using Cloud Computing Technology In an era of shrinking budgets, it gets harder with each passing year to justify the purchase and maintenance of servers that aren’t in use almost all the time. Cloud computing24 offers price savings due to economies of scale and the fact that you’re only paying for the resources you actually use. Organizations of all sizes can take more risks when it comes to creative, innovative technology ideas when the new application will run on someone else’s infrastructure. Digital libraries do not have to decide between devoting their limited server resources to the OPAC’s overflow traffic and a new mobile web application that one of your colleagues wants to develop. If they’re both hosted in the cloud, the resources devoted to each will shrink and expand as traffic rises and drops. Furthermore, creating and configuring new virtual server instances is fast and easy in the cloud. Digital libraries may soon be building and managing their own data centers. In addition to all the hype and optimism surrounding cloud computing, there are still significant fears and doubts Industry Challenges points out. In particular, the major cloud computing vendors haven’t yet fully addressed concerns about 102 | II JJ O OD D LL SS
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security, privacy and reliability. These concerns are leading some companies to build their own private or hybrid clouds. A hybrid cloud25 is primarily based in a privately-owned and operated data center, but it can shift some of its traffic and data processing requests to public cloud vendors such as Amazon or Rack space on an as needed basis. This hybrid model would let digital libraries maintain more control over the applications and data stores that contain sensitive, private information about patrons. Moreover, digital libraries can continually adjust and fine-tune the balance between the tight control of a private Information Technology infrastructure, and the flexibility and savings of cloud-hosted infrastructure. Just as digital libraries presently cooperate with one another to buy Information Technology equipment, bandwidth and the services of Information Technology professionals, Digital libraries may soon cooperate in the building and management of data centers. Alternately, if enough digital libraries express interest, a company such as Google, Amazon, Microsoft or another cloud vendor might create a digital library Cloud similar to Google’s Government Cloud. Or, a library vendor with deep Information Technology resources (e.g. OCLC or Sirsi Dynix) might build digital library-centric cloud services on top of cloud infrastructure leased from one of the more established players. CONCLUSION Cloud computing represents an exciting opportunity to bring on-demand applications to Digital Library, in an environment of reduced risk and enhanced reliability. However, it is important to understand that existing applications cannot just be unleashed on the cloud as is. Careful attention to design will help ensure a successful deployment. Certainly cloud computing can bring about strategic, transformation and even revolutionary benefits fundamental to digital libraries. For organizations providing digital libraries with significant investment in traditional software and hardware infrastructure, migration to the cloud will bring out considerable technology transition; for less-constrained organizations or those with infrastructure nearing end-of-life, adaptation of cloud computing technology may be more immediate.
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Paul Young (1994) Assistant Director, directorate for computer and information science and engineering. National Science Foundation NSF Announces Awards for Digital library Research. NSF PR 94-52, Washington, D.C: NSF.
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Michael Miller. Cloud computing: Web-based Applications That Change the Way You Work and Collaborate Online [M].Que Publishing, 2008
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Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, James Broberg, Ivona Brandic, “Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility,” Future Generation Computer Systems, Volume 25, Issue 6, June 2009, pp. 599616.
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Maggiani, R. “Cloud computing is changing how we communicate,” IPCC 2009, July 2009, pp. 1-4
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Brian Hayes.(2008) Cloud computing. Communications of the ACM, July (7):p9–11
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Roy Bragg. (2008) Cloud computing: When computers really rule. Tech News World, July Electronic Magazine, available at http://www.technewsworld.com/story/63954.html.
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Yang Mingfang, Yuan Xilin. (2009) Digital Libraries under the Cloud Computing Environm [J). Library Development, (9).
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John W. Rittinghouse and James F. Ransome (2010), Cloud Computing Implementation, Management and Security, CRC Press, Taylor & Francis Group, Supra Note 1, p30. ISBN: 978-1-4398-0680-7
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John W. Rittinghouse and James F. Ransome (2010), Cloud Computing Implementation, Management and Security, CRC Press, Taylor & Francis Group, Supra Note 1, p34-35. ISBN: 978-1-4398-0680-7
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John W.Rittinghouse and James F.Ransome (2010), Cloud Computing Implementation, Management and Security, CRC Press, Taylor & Francis Group, Supra Note 1, p44. ISBN: 978-1-4398-0680-7
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John W. Rittinghouse and James F.Ransome (2010), Cloud Computing Implementation, Management and Security, CRC Press, Taylor & Francis Group, Supra Note 1, p48. ISBN: 978-1-4398-0680-7
12)
John W. Rittinghouse and James F.Ransome (2010), Cloud Computing Implementation, Management and Security, CRC Press, Taylor & Francis Group, Supra Note 1, p50. ISBN: 978-1-4398-0680-7
13) Use of SaaS in libraries available at: http://serialssolutions.com 14) Electronic journals on http://www.libguides.com
SaaS
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15) Catalogue of EC2 futures available at: http://aws.amazon.com/ec2/ 16)
Wheeler, B., & Waggener, S. (2009). Above Campus Services: Shaping the Promise of Cloud Computing for Higher Education. Educause Review, 44(6), 52-66. (COinS)
17)
Breeding, M. (2009). The Advance of Computing From the Ground to the Cloud. Computers in Libraries, 29(10), 22-25. (COinS)
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Fox, R. (2009). Library in the clouds. OCLC Systems & Services, 25(3), 156-161. doi: 10.1108/10650750910982539. (COinS)
19)
Leong, L. (2009, June 16). Software on Amazon’s Elastic Compute Cloud: How to Tell Hype from Reality. Gartner Research. Retrieved January 25, 2010, from http://my.gartner.com/ portal/server.pt?open=512&objID=260&mode=2&PageID=3460702&res Id=1022715&ref= QuickSearch&sthkw=cloud+computing+tco
20)
Hype Cycle for Cloud Computing, 2009. (2009, July 16). Gartner Research. Retrieved January 25, 2010, from http://my.gartner.com/portal/server.pt?open=512&objID=260 &mode=2&PageID=3460702 &resId=1078112&ref=QuickSearch&sthkw=cloud+ computing +cost
21)
2009 Horizon Report | EDUCAUSE. (2009, January 20). Educause. Retrieved January 25, 2010, from http://www.educause.edu/ELI/2009HorizonReport/163616
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23) Yang Jie and Liu Wanjun (2010) Cloud Computing in the application of Digital Library, International conference on Intelligent Computation Technology and Automation, IEEE: DOI 10.11109/ICICTA.2010.39. Available at: www.ieeexplore.ieee.org. 24) Future improvement of Cloud Computing in digital library available at: Cloud Computing in Libraries\Materials\What is Cloud Computing and how will it Affect Libraries Tech Soup for Libraries.htm 25) Hybrid cloud computing available at: http://cloudcomputing.sys-con.com.
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GLOBAL VILLAGE: MOBILE ACCESS TO LIBRARY RESOURCES
Library Hi Tech Global village: mobile access to library resources Hoivik Jingru,
Article information:
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Global village: mobile access to library resources
Global village
Jingru Hoivik IT Department, National Library of Norway, Oslo, Norway
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Abstract Purpose – This paper describes an ongoing mobile library project for the National Library of Norway. The purpose is to develop a visual web service prototype providing mobile access to the library’s growing repository of digital data. Design/methodology/approach – The author has set up several test databases and developed a number of Android applications (apps) for testing purposes: image presentation; library’s digital exhibition “in the pocket”; traditional free text search; location based search; barcode scan for ISBN search / QR encapsulation; and voice/spoken search. Findings – These six approaches were found to be promising using mobile technology. Cloud technology has changed the mobile phone from a voice transmitter to a multi-purpose device connected to the network. Library resources may now be distributed in the cloud, with global mobile access, to really achieve a global library network. Originality/value – The applications developed here are innovative and unique, and will add to the general body of use cases.
467 Received 3 December 2012 Revised 10 January 2013 21 January 2013 Accepted 25 January 2013
Keywords Mobile learning, Digital curation, Android apps, Spoken search, Location based search, Cloud technology, Libraries, Norway Paper type Research paper
Introduction The new electronic interdependence recreates the world in the image of a global village (McLuhan, 1962, p. 31).
The prescience of McLuhan’s writings is striking, even though he did not foresee the rise of the internet. But 50 years later the network has indeed created a framework to sustain a virtual global village. Using cloud technology we are no longer dependent on specific schemata and coding systems for the library field as such (even though they are still useful), but may share library resources on a global level with generalized protocols and access mechanisms. Access to online textual resources rapidly becomes commonplace. The National Library of Norway is a basic supplier of information resources about Norway and the history and cultural heritage of this country; – collecting, archiving, organizing and distributing a wide range of materials. The documents date back to the Middle Ages and are based on a variety of physical carriers – paper for sure but also materials like parchment, glass plates, vinyl records and other “old” formats. Today, the central pillar of the library collection is the Legal Deposit Act. This ensures that virtually everything that is published in Norway can be found in the National Library. The Library is also responsible for the national bibliography, a catalog of all Norwegian literature, which acts as a key to the important parts of these collections.
Library Hi Tech Vol. 31 No. 3, 2013 pp. 467-477 q Emerald Group Publishing Limited 0737-8831 DOI 10.1108/LHT-12-2012-0132
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The National Library of Norway is now in the process of digitizing all its collections in order to establish a Digital National Library. These efforts play a key role in the country’s digital library strategy. The library goes beyond print: it preserves digital signals as well. Data from four national radio and TV channels are transferred directly to the library every night. The library’s goal is to become and outstanding multimedia knowledge center among Europe’s modern national libraries. By now, more than 100,000 digital books have been made accessible through its website. Every week about 1,000 additional volumes are digitized. Providing online access, especially mobile access to these data, is of paramount concern. The number of mobile users is growing at an exponential rate. The mobile devices are going to be the primary connection tools and interfaces to the internet in the future. We do not know yet how powerful this growth could be, but libraries must take the challenge in serious. Image presentation on mobile Mobile devices are increasingly used to access and display multimedia data such as digital images, sound recordings and video. Due to some limitations of current mobile devices, such as screen size and resolution, different strategies have been used when we design the screen renditions for our mobile users. (1) Focus on essentials. Figure 1 shows the relationship between the rendition of a single page on a PC screen and the same material as presented in the mobile version. The image itself (grayed out) is a facsimile of a well-known song. This, which constitutes the main content of the presentation, is kept on the mobile version. The rest of the PC-oriented web page, i.e. the title and explanatory text, has been removed. The latter material was kept, but presented differently on the mobile device, for instance as a voice-over or as text on a separate screen. The logo of the library was moved to the top of the image, while the button for playing the song (the yellow square in Figure 1) was redesigned. See separate bullet point on Buttons. (2) Large image: When large images have to be displayed, the system resources sometimes exceed the capabilities of a mobile device. Due to some limitations of
Figure 1. The same material as presented on a PC and mobile phone screens
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current mobile devices, screen size and capabilities, the following modifications were introduced (see Figure 2): . Resize the large image to a size which is suitable for target mobile screens. . Two steps to display images: Show a smaller sized image, with a good fit to the mobile screen which gives an overview of the content. When the user clicks this image, the original image is displayed. . Using the mobile touch screen to study the larger (original) image: – Pan to explore different parts of the image by finger swiping. – Zoom to get more detailed views of the image by finger pinching. Buttons: The normal button size for PC mouse pointers is normally unsuitable for mobile touch screens, either because they become too small for fingers to touch or too insensitive and unresponsive to the touch. The solution may be to design larger buttons for mobile versions or to define a hidden, and larger, responsive touch area (“hidden button”) behind the visible button image. In Figure 1 a hidden button has been placed behind the image. When the user clicks on it the whole image reacts as a button. It will bring on the next step of the presentation and start to play the song at the same time. Sound: Multimedia presentations with image slide shows are particularly useful for mobile users. In the previous example (Figure 1) several images from the original collection related to the famous Norwegian song “Dan fyrste songen” have been included. Sound files have been synchronized with the pictures, which include old newspaper clippings, photographs of the author and the original score. Three dimensions: One exciting usage domain for the mobile users, especially for young people, is the possibility for exploration and object manipulation opened up by mobile technologies. 3D objects may be explored by rotation, zoom functions etc. Figure 3 shows mobile device with a simulated 3D animation of football field from our sports collection. Interactivity and games: The library has a varied and extensive collection, but the online environment is strongly competitive, with many different players. It
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Figure 2. Page-flipping function
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Figure 3. Example: 3D animation
is urgent for the library to develop interesting and interactive solutions based on its materials. In this respect, games may appeal to users’ curiosity. They can be played anywhere. Let me use a map as a representative example. The library has an extensive collection of maps, including the first known map of the Nordic area. It was published in 1482. The map depicts how Europe may have been perceived and how maps were actually drawn in the 15th century. We have used this material to create a puzzle (Figure 4) for both PC and mobile users. The map is shown in vague outline and as twelve separate and moveable jigsaw pieces. When the user has successfully placed all the pieces where they belong above the outline, the game enters the second phase. More detailed information about the original map is then displayed. Digital exhibition in the pocket The National Library has an ongoing program for purely digital and combined physical/digital exhibitions. Every three to four months a thematic exhibit is organized by the Exhibitions and Anniversaries Section of the Library in the main exhibition hall.
Figure 4. Example: Game-based learning
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On display are physical artifacts about the chosen topic. In addition some of these materials are represented in digital formats as still and moving images (videos, animations) and other presentations formats for touch screens. (Figure 5) The challenge here is mobility. How can we transfer digital presentations from the large exhibition touch screens to mobile devices? How can we create “mini” exhibits so that our users may play our digital shows outside the library and show exhibition videos to their friends on their mobile phones? Figure 6 presents a mind map structure for the relationship between items in the physical collection, the touch screens in the exhibition hall (see left square in the diagram), PCs and mobile devices. The QR (Quick Response) code in the center of the diagram illustrates a core function. This is used to align or connect between different accessing modalities. Mobile users may access the digital exhibition by scanning the QR code that is placed on the wall of the touch screen box or beside a physical exhibit. Content is immediately conveyed by the wireless network in the exhibition hall and rendered on their mobile phone or e-pads. The m-users can also download exhibition apps on their mobile devices by scanning a similar QR code.
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Figure 5. A digital exhibition at National Library of Norway
Figure 6. A mind map structure for the exhibition display surfaces
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The exhibition PCs are integrated in exhibition boxes with touch screen surfaces on the top (Figures 7 and 8). In Figure 7, a user is scanning the QR code on the touch screen box using her mobile phone. Figure 8 a user is browsing the image presentation on a pad (10” Galaxy Tab) after scanning the QR code. The users can also scan the QR code and download the presentations/videos on his/her mobile device and play them later anywhere outside the library.
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472 Mobile search To explore mobile search facilities we have made several test databases on the library’s server. These may be accessed from prototype Android applications (apps). At this stage four different approaches have proved promising[1]: (1) free text search; (2) searching by ISBN/barcode scanning;
Figure 7. A user scanning a QR code in the exhibition hall
Figure 8. A user is browsing the ensuing image presentation on a pad
(3) location based search; and (4) voice/spoken search.
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The following is a brief walk-through for each of these approaches. 1. Free text search Free text search in situ is equal to traditional computer searches with the benefit of geographical contextualization. The challenge is to develop item descriptors that respond well to probable requests and to implement solutions in new computational environments – in this case using App Inventor for Android. Databases are an important component in mobile search and web applications for store and retrieve data. Google provides a built-in database on Android devices and associated with custom apps. This design allows the app to store and retrieve information via a simple Application Programming Interface (API) with the StoreValue and GetValue functions: . StoreValue: Store data to the database each time the user submits a new value. . GetValue: When the app launches, load the data from the database into a variable. Google has created a framework that allows a user-defined app to store data on the web database which runs on Google’s App Engine. It is also possible to access other repositories, but this also requires a call-through via the web database to the Google server. This architecture is somewhat complicated and probably mandated by security and possible commercial concerns. In our case the design was based on a separate MySQL database on our library’s own server park, but with the addition of several PHP scripts that establish communication through the Google intermediary. These are compliant with App Inventor’s communication protocols. 2. Searching by ISBN It is not easy to type on a mobile phone’s touch screen and users easily make typing errors. A great built-in Android function is related to Barcode Scanning. With Quick Response (QR) encapsulation of pertinent information, a physical artifact may be enhanced with digital descriptors or references that are decoded by the mobile device. To exploit this option, we need to build our app so that it activates the Barcode Scanner on the mobile device, transfer the relevant search profile to the database and retrieves the results for display on the portable device. Our Android app has a simple user interface that lets the mobile user enter a book’s ISBN number. The program looks up and lists the corresponding title, author, ISBN and publication information from the library’s catalog. Additionally, the app can active the Barcode Scanner on Android mobile devices so the users can scan a book’s barcode to trigger the search directly without manually typing the ISBN number (See Figure 9). 3. Location based search In location search, one may exploit the fact that mobile devices are “geographically aware” and may use geographical coordinates to enhance search and retrieval applications with contextual information.
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Figure 9. Searching ISBN by barcode scanning
Figure 10. Location-based search using location sensor
Google Android software provides a mobile user’s exact location information on Google Maps via the Google Latitude application (Google, 2012). This information is automatically updated when he/she moves around. Google Latitude is an application that brings location tracking to user’s mobile phone even without the General Positioning System (GPS). If a user is travelling with a mobile phone that has Google Latitude installed, his/her friends can track the exact location on Google Maps through a mobile device or any internet connected computer. The location sensor in Android allows us to create Android applications that utilize the Android phone’s location capabilities. This function provides current location information by latitude and longitude numbers. This is an advanced feature in Android system that could make it possible to integrate location sensor directly into our own Android applications. The application design steps are as follows: . determining the location of an Android mobile device using the Location Sensor; . recording the location data in a database directly on the device; . user’s exact location information by latitude and longitude numbers is sent to the searching window in the app’s interface; . the procedure will (1) search for the location number string in our test databases on our library server; (2) return the result with the matching search string, and (3) show the result set on mobile screens (Figure 10); and
.
using the ActivityStarter to open Google Maps from our app and show the current location.
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When a mobile user clicks on the “location” button in our search app interface, the user’s exact latitude and longitude parameters appear in the search window via our search application (Figure 10). The result, together with the users search string from our database, is shown on the mobile screen. This facility will enable mobile users to search dynamically for library’s collection in the nearby area where he/she currently located.
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4. Voice/spoken search More and more users use their mobile devices to stay in touch with their friends, families and coworkers wherever they are. They are not only talking over the phone, but also use messaging, emailing, chatting, photo sharing and blogging systems. This habit of “talking to the phone” may enhanced by voice/spoken input for other purposes as well. Dictation of commands and search profiles liberates the user from manual and dexterity constraints. Voice/spoken input also allows us to make our applications more interactive and fun. We can use the Text to Speech component in Android to convert voice/spoken input into text strings. The Voice Recognition component allows users to speak to their mobile phone. The sound bite is converted into a text, and that text will be used in the program to retrieve items from a repository. Google’s Voice Search application could respond to a recognizer-intent by displaying the “Speak now” dialog and streaming audio over WiFi or 3G/4G networks to Google’s servers. As Android developers, librarians may integrate voice/spoken capabilities into their Android applications. The Android Software Development Kit (SDK) makes it possible to integrate users’ speech-input directly into our own application. The application uses the startActivityForResult() function to broadcast an intent that requests voice recognition, including an extra parameter that specifies the language model. The voice recognition application that handles the intent processes voice input and sends the recognized string back to the application (Figure 11). Then a PHP Scripts is called which activates a stored procedure that will search for that string in the test databases. This test application then retrieves and returns the relevant values from the database as a list of the objects that contain the searching string. The
Figure 11. Spoken search using text-to-speech component
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Figure 12. Global access
result is shown on the users’ mobile screens. Mobile users will benefit from having this facility on mobile: . freed from typing on limited screen size; . voice-to-text input in database for commenting, blogging and so on; and . live recording to store text input of important information. Library in the Cloud In the location-based search and voice/spoken search we exploit the possibilities inherent in cloud computing. Cloud technology is a set of network devices designed specifically for the storage and transmissions of a great variety of data. All devices are synchronized with up to date content at any time. Cloud technology allows users to store their work files on cloud servers, which can then be edited from any computer with an internet connection. Innovation changes technology and technology changes the mode of service. Cloud technology is currently changing the way we read, store and transfer data, including the modes of interaction with library services. Cloud technology is a revolutionary initiative that will bring disruptive changes to the library (see Figure 12). There are actually many such services available like music streaming and online photo/document sharing. The spoken search mentioned above is one example. It is based on the Google’s speech recognition system, which is stored on Google’s cloud server. In general cloud technology is more suitable for mobile devices. Mobile phones and pads have been limited in terms of storage and processing capacity. Some of the storage and processing loads may then be relegated to the cloud servers. But such limitations are currently being overcome on high end devices. More important is the fact that cloud technology support ubiquitous computing using mobile devices. Cloud technology has thus changed the mobile phone, from communications equipment into a handheld device connected to the network.
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There are several different platforms on both for desktop and mobile. They compete ferociously for the time, attention and purchasing power of users. Consumers may carry more than one device which may run different operating systems. The perfect synchronization scenario is that every device knows what the user has been doing on the other devices and can recreate the same context on any other device. This is currently being the case with digital books from Amazon. One can stop reading on one page on one device – and the book is opened on the same page on all the others. Such functionality is also a challenge for the library field. By using cloud technology, libraries and especially the smaller ones no longer need to configure complex computer system and other infrastructure for themselves. They can get rid of physical equipment and its technical constraints and drop the burden of software maintenance. Instead they can focus more on library’s original tasks: services and innovation. Now network has become a virtual global village. Library resources may be distributed in the cloud, with global mobile access, to really achieve a global library network. Using cloud technology, sharing library resources global – a simultaneous happening is becoming true. The basic usage of “cloud” is to store and to share. Data are stored in the cloud servers, that anybody free to use on any kinds of devices at anytime, anywhere. This is exactly consistent with digital libraries’ vision. Note 1. A test version of mobile search is available on YouTube, available at: www.youtube.com/ watch?v ¼ o3KtUTHmDbc References Google (2012), Android Developers, available at: http://developer.android.com/. McLuhan, M. (1962), The Gutenberg Galaxy, Routledge & Kegan Paul, London. Mednieks, Z. and Dornin, L. (2011), Programming Android, O’Reilly Media, Sebastopol, CA. Steele, J. and To, N. (2010), The Android Developer’s Cookbook, Pearson Education, Boston, MA. Further reading Gurley, B. (2012), “Why dropbox is a major disruption”, Above the Crowd, February, Vol. 23, p. 2012. Lund, H.O. and Berg, S.F. (2012), Norske polarheltbilder 1888-1928, Forlaget Press, Nasjonalbiblioteket. Rogers, R. and Lombardo, J. (2011), Android Application Development: Programming with the Google SDK, O’Reilly Media, Sebastopol, CA. Rosenberg, J. and Mateos, A. (2011), The Cloud at Your Service, Manning Publications, Greenwich. Corresponding author Jingru Hoivik can be contacted at: [email protected]
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This article has been cited by: 1. MansouriAli, Ali Mansouri, Soleymani AslNooshin, Nooshin Soleymani Asl. Assessing mobile application components in providing library services. The Electronic Library, ahead of print. [Abstract] [Full Text] [PDF] 2. RodriguesCharles, Charles Rodrigues, Godoy VieraAngel Freddy, Angel Freddy Godoy Viera. 2018. Criteria for adoption of e-books in libraries in the context of the paradigm of cloud computing. Information Discovery and Delivery 46:3, 161-172. [Abstract] [Full Text] [PDF] 3. Pakdaman NaeiniMaryam, Maryam Pakdaman Naeini, Sharif MoghaddamHadi, Hadi Sharif Moghaddam, ZiaeiSoraya, Soraya Ziaei, GhaebiAmir, Amir Ghaebi. Mobile services in the libraries of the world’s top universities. Library Hi Tech, ahead of print. [Abstract] [Full Text] [PDF] 4. HuangTien-Chi, Tien-Chi Huang, ShuYu, Yu Shu, YehTing-Chieh, Ting-Chieh Yeh, ZengPei-Ya, PeiYa Zeng. 2016. Get lost in the library?. The Electronic Library 34:1, 99-115. [Abstract] [Full Text] [PDF] 5. Fei Yao, Chengyu Zhang, Wu Chen. 2015. Smart talking robot Xiaotu: participatory library service based on artificial intelligence. Library Hi Tech 33:2, 245-260. [Abstract] [Full Text] [PDF]
IAAS CLOUD COMPUTING SERVICES FOR LIBRARIES: CLOUD STORAGE AND VIRTUAL MACHINES
OCLC Systems & Services: International digital library perspectives IaaS cloud computing services for libraries: cloud storage and virtual machines Yan Han,
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IaaS cloud computing services for libraries: cloud storage and virtual machines Yan Han The University of Arizona Libraries, Tucson, Arizona, USA
IaaS cloud computing services 87 Received 2 September 2012 Revised 29 October 2012 Accepted 20 November 2012
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Abstract Purpose – The purpose of this article is to provide an overview of current uses of cloud computing (CC) services in libraries, address a gap identified in integrating cloud storage in IaaS level, and show how to use EC2 tools for easy backup and resource monitoring. Design/methodology/approach – The article begins a literature review of CC uses in libraries, organized at the SaaS, PaaS and IaaS levels. The author presents his experience of integrating cloud storage services S3 and GCS. In addition, he also shows how to use virtual machine EC2 tools for backup and monitoring resources. Findings – The article describes a case study of integrating cloud storage using S3 and GCS. S3 can be integrated with any program whether the program runs on cloud or locally, while GCS is only good for applications running on GAE. The limitation of the current GCS approach makes it hard to use for a stand-alone cloud storage. The author also discusses virtual machines using EC2 and its related tools for backup, increase storage, and monitoring service. These services make system administration easier as compared to the traditional approach. Research limitations/implications – The article presents current CC uses in libraries at the SaaS, PaaS, and IaaS levels. CC services are changing quickly. For example, Google has stated that its APIs are experimental. Readers should be aware of this. Practical implications – The author shows his experience of integrating cloud storage services. Readers can understand the similarities and differences between S3 and GCS. In addition, readers can learn the advantages and concerns associated with implementing cloud computing. Readers are encouraged to consider questions such as content, skills, costs, and security. Originality/value – There are many uses of CC services in libraries. However, gaps are identified: in IaaS cloud storage, a few libraries used Amazon S3 and Microsoft Azure, but none explored using Google Cloud Storage (GCS); none provided implementation details, difficulties, and comparisons of S3 and GCS; and a few articles have briefly discussed implementations on Amazon EC2, but have not provided specific details about upgrade and backup. This article addresses those gaps. Keywords Cloud computing, Google cloud storage, Cloudwatch, Libraries, Online operations, Virtual worlds Paper type Case study
1. Introduction Cloud computing (CC) is gaining popularity not only in libraries, but also in other industries. Over the years, CC providers have continued to improve their infrastructure to enhance existing services, and at the same time introduce new services addressing every aspect of computing. For example, Elastic MapReduce for Apache Hadoop, released in 2009, supports large data sets and data-intensive distributed applications. Amazon GovCloud, introduced in August 2011, is specially designed to allow US government agencies using CC for sensitive data to meet compliance requirements. In
OCLC Systems & Services: International digital library perspectives Vol. 29 No. 2, 2013 pp. 87-100 q Emerald Group Publishing Limited 1065-075X DOI 10.1108/10650751311319296
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June 2012 Google offered Google Compute Engine, allowing users to run large-scale computation on Google’s infrastructure. Most recently, Amazon announced Glacier in August 2012 (Amazon, 2012b) as a low-cost archive service in supplement to its cloud storage service S3. It is necessary to understand what CC is, and its service models. The National Institute of Standards and Technology published the following final definition of cloud computing: Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics, three service models, and four deployment models” (Mell and Gance, 2011).
The three service models are defined as: (1) Software as a service (SaaS) allows users to use the provider’s applications on a cloud through a web browser or an application programming interface (API). The provider manages almost everything in the cloud infrastructure (e.g. physical servers, network, OS, applications). End users can run applications, but do not control the cloud infrastructure (Mell and Grance, 2011). The SaaS primary users are the general public. Gmail, Google Drive, Google Calendar, Windows SkyDrive, and Dropbox are popular SaaS services. (2) Platform as a service (PaaS) allows users to deploy their own applications on the provider’s cloud infrastructure under the provider’s environment, such as programming languages, libraries, and tools. The end users can control their own applications, but do not have control of the cloud infrastructure (Mell and Grance, 2011). PaaS is targeted directly to software developers, who develop, test, and run applications on a PaaS platform. Google App Engine (GAE) is a PaaS service. (3) Infrastructure as a service (IaaS) allows users to control and manage computing resources (e.g. storage, networks, computing power) so that they can deploy and run arbitrary software (Mell and Grance, 2011). Providers only manage underlying physical cloud infrastructure (e.g. physical servers and network). The users have maximum control of the infrastructure, as if they own the underlying physical servers and network. IaaS is primarily targeted at use by enterprises and integration with data and applications. IaaS providers include Amazon, Google, Microsoft, Rackspace and IBM. 2. CC in libraries CC offerings range from hundreds of SaaS applications and storage services to only a few virtual machine and storage IaaS services offered by big IT companies. Libraries are in the business of creating, managing and delivering information. Almost all libraries have their own websites, maintain integrated library systems (ILS), provide access to digital collections through repositories, and maintain storage and backup of their content and data. A subject search of “Cloud Computing” and “Libraries” in the Library Literature and Information Science Full Text database showed that 80 results were found, of which 40 were peer-reviewed articles published since 2008. These
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articles addressed public to academic libraries and concerned topics ranging from websites to repository systems. Libraries have been using SaaS and IaaS services since 2009. An analysis of these articles reveals that libraries are interested in: . SaaS services (e.g. office applications, Google Doc, calendar, scheduling applications, and cloud storage for synchronizing and backup files) for daily work; and . IaaS services (e.g. virtual machines and cloud storage) for content delivery such as websites, repositories, and online backup. Gaps are also identified: . for IaaS cloud storage, a few libraries used Amazon S3 and Microsoft Azure, but none had explored using Google Cloud Storage (GCS); . in addition, no articles provided implementation details, difficulties, and comparisons of S3 and GCS; and . with regard to IaaS virtual machines, a few articles briefly discussed implementations on Amazon EC2, but did not provide specific details about upgrades and backup. The author feels that these gaps are important to understand the full range of available IaaS services, and also to understand the differences between these services to make an informed decision. 2.1 Uses of SaaS services in libraries Libraries have been using SaaS services in almost all aspects of library work, from instruction to scheduling to regular office applications. Some SaaS services are built on top of PaaS and/or IaaS platforms. For example, Dropbox uses S3 for its storage. DuraSpace uses cloud storage services such as S3 and Microsoft Azure. The SaaS services used by libraries include Gmail, Google Calendar, Google Docs/Drive, Dropbox, and the OCLC WorldShare Management Service. A literature review shows that libraries used the following SaaS services: . the Eastern Kentucky University Library used Google Docs and Google Calendar for instruction (Kroski, 2009); . the District of Columbia Public Library used Google Docs for staff (Tonjes, 2010); . library discovery systems such as Summon, Primo, and WorldCat Local utilized CC (Breeding, 2011); . Murray State University used Dropbox for instruction (Bagley, 2011); . New York City College of Technology used Google Calendar for instruction scheduling and daily work (Leonard, 2011); . the University of Wisconsin-Eau Claire used Google Forms for reference and instruction (Miller, 2011); . Union Presbyterian Seminary librarians utilized Ning, a social network site, for summer courses (Deeds et al., 2011); . the University of Washington used the free web conferencing software Dimdim, but later it became hard to switch due to its acquisition by another company (Gleason, 2011);
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VoiceThread was used for collaboration and library instructions by many institutions (Ditkoff and Young, 2011); the Kindura project is using DuraCloud to interact with cloud storage (Waddington et al., 2012); Davidson College (Milberg, 2012) and the Australian public library systems (CILIP Update, 2012) presented analysis, evaluation, selection, and/or cost to use the cloud-based OCLC WorldShare Management Service; and many libraries are using Google Analytics.
2.2 Uses of PaaS and IaaS services in libraries The most popular IaaS and PaaS services include Amazon Web Services (AWS) such as EC2 and S3, Google App Engine (GAE), the GCS, Rackspace Cloud Files and Microsoft Azure. A literature review shows that libraries have been using IaaS and PaaS services since 2009, mostly in utilizing virtual machines such as EC2 and cloud storage such as S3 and Microsoft Azure. EC2 is basically an IaaS scalable virtual machine service. It is the most popular IaaS service used by libraries to host websites, repositories, and integrated library systems (ILS), because it gives users complete control of virtual resources and is easily scalable. Working as a central hub, an EC2 instance can be integrated with other CC services to improve effectiveness and efficiency. . The University of Arizona Libraries reported using EC2 instances, Linode instances, and GAE for a DSpace repository, an ILS, and the Afghanistan Digital Library’s websites (Han, 20011). . OhioLink implemented AWS for their repository (Kroski, 2009). . The District of Columbia Public Library utilized EC2 instances to host its website and image archive (Tonjes, 2010). . The Z. Smith Reynolds Library at Wake Forest University used EC2 for its ILS (Mitchell, 2010). . The University of Arizona Libraries reported two cases of total costs in running a DSpace instance using EC2. The cost of an EC2 instance was more cost-effective than a traditional server, and using S3 cloud storage might not be cheaper than local storage (Han, 2011). . Two librarians wrote how to put Koha in the Cloud (Nighswonger and Engard, 2011). . OCLC offered CC web services for data sharing and reuse (Coombs, 2011). . The Ohio University system and private colleges built DSpace repositories with Amazon (Davison, 2011). . Rice University evaluated CC and its usefulness for the library IT department, and it considered which types of project are good candidates for CC and which are not. Glavin and Sun presented their case of using EC2 instances. They found that some projects were better suited to CC than others. Flexibility and cost savings were the best reason to use CC. However, there were also good reasons not to move some projects into CC (Galvin and Sun, 2012).
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Cloud storage services include Amazon S3, GCS, Rackspace Cloud Files, and Microsoft Azure. Libraries have been exploring using on-demand cloud storage since 2009. Several cases have been reported to use S3, while one case implemented Rackspace and Microsoft Azure. In comparison, there is no reported use of GCS in libraries: . the Library of Congress and DuraCloud launched a pilot program to test the use of CC (Library of Congress, 2009); . Central Connecticut State University Libraries used Amazon S3 for digital preservation (Iglesias and Meesangnil, 2010); and . DuraCloud stated that it uses Amazon S3, Rackspace Cloud Files, Microsoft Azure, and San Diego Supercomputer Center (SDSC) cloud storage (Branan, 2011). Besides using commercial providers, several institutions have launched their own private clouds, including: .
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Hochschule Furtwangen University in Germany built a private cloud infrastructure, and ran e-learning applications on it. A paper by Doelitzscher et al. (2010) concludes that CC gives flexibility and better management of resources. The University of Southern California (USC) deployed Nirvanix private cloud storage of 8 petabytes of data for its digital repository (USC Shoah Foundation, 2011). SDSC launched the largest academic cloud storage (San Diego Supercomputer Center, 2011).
3. Integrating IaaS cloud storage 3.1 Cloud storage in SaaS and IaaS: one provider, two service offers Cloud Storage providers are generally large IT companies such as Apple, Amazon, Google, Nirvanix, Rackspace, and Microsoft. Amazon, Google and Microsoft are some well-known cloud storage providers in both the SaaS and IaaS areas. As mentioned before, SaaS services are primarily used by the general public. Popular SaaS cloud storage services are Apple iCloud, Amazon Cloud Drive, Google Drive, DropBox, SugarSync and Microsoft SkyDrive. Some SaaS cloud storage providers are building their services on top of the IaaS providers. For example, both Dropbox and SugarSync use S3 as backup or backend. IaaS cloud storage providers are limited to these large IT companies, offering services such as S3, GCS and Microsoft Azure. In most cases, both IaaS and SaaS cloud storage services provide APIs for accessing data. For example, Google SaaS storage (Google Drive) and its IaaS storage (GCS) have different APIs. The line between the SaaS and IaaS levels is sometimes fuzzy, but the two levels serve different purposes: IaaS for enterprise and SaaS for individuals. Cloud storage in IaaS is intended for developers to store and access data primarily through an API for enterprise-level applications, while SaaS cloud storage is for individual use and private content sharing. Uses of IaaS cloud storage include online archives, backups, and big data. In comparison, typical uses of SaaS cloud storage are personal file synchronization, share and backup. The above literature review shows that cloud storage in SaaS such as Google Drive and Dropbox are popular in libraries for individual uses in instruction and daily work. Regarding integrating IaaS cloud storage for online storage and archiving, there are only a few cases reported of using
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S3, SDSC cloud storage, and Windows Azure, without much detail. Neither reported using GCS, nor provided a comparison of these IaaS services. 3.2 Integrating IaaS cloud storage: Amazon S3 and Google Cloud Storage Amazon S3 is an IaaS cloud storage service through web services interfaces such as REST and SOAP. It was launched in 2006 and has reduced its prices over the years. Many services such as Dropbox, Ubuntu One, and Posterous use S3, while library uses of S3 include DuraCloud and Central Connecticut State University Libraries. Quint (2008) and Murray (2008) discussed issues and costs when comparing the OCLC Digital Archive with S3, and suggested that it was an apples-to-oranges comparison. Their article claimed that S3 missed critical components of preservation systems such as access control and a content backup/restore facility in functionality, while they agreed that S3 was much cheaper in cost (Quint, 2008; Murray, 2008). Murray also suggested that S3 lacked functions for digital preservation such as fixity checks, format verification and related digital preservation metadata (Murray, 2008). Amazon claims that data in S3 is secure and that access is highly reliable at 99.99 percent availability. Amazon’s Identify and Access Management (IAM) is integrated with these AWS (Amazon, 2008, 2012a). The Central Connecticut State University Library developed a system using S3 for digital preservation storage. They compared the cost of using OCLC Digital Archives with that of S3 for 5 TB data storage, and came to a conclusion that S3’s cost is only one third of that of the OCLC Digital Archives (Iglesias and Meesangnil, 2010). GCS, announced in May 2010, is a late competitor in IaaS cloud storage. Access to GCS can be through the RESTful interface or through its API for GAE (Google, 2012a). In comparison, Nirvanix started its Storage Delivery Network in 2007. In 2011, Nirvanix and USC deployed over 8 petabytes of private cloud storage for the USC digital repository and other units for archiving high-resolution videos and audios (USC Shoah Foundation, 2011). To the author’s knowledge, this is the largest private cloud storage deployed for libraries. Due to its higher cost, the author did not consider Nirvanix. The author and a student developed a digital preservation program called “Archivebox” trying to utilize both S3 and GCS. The software uses S3 for on-demand storage for files, directories and related metadata. The official documentation seems inadequate and was dated 2006. Although sometimes coding with the API is trial-and-error, the data model and its API are very simple, and the integration of functions (e.g. create, upload, and download) is simply a few lines of Java code. The program eventually comes with fewer than one hundred lines of code in the integration of S3. In comparison, the author encountered difficulties in the process of integrating the GCS, and found it is difficult to use GCS without running on GAE. After some research, the author believes that integrating the GCS will take too much effort. Its current API and integration is not as simple as that of S3. 3.3 Similarities S3 is intentionally built with simplicity in mind. Its data model consists of buckets and objects: A bucket is a container for objects stored in Amazon S3. Every object stored in Amazon S3 is contained in a bucket. [. . .] Within a bucket, you can use any names for your objects, but bucket names must be unique across all of Amazon S3 (Amazon, 2006).
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The simplicity of this model makes it flexible in implementing a variety of services such as a traditional hierarchical file/directory structure. On the other hand, it also means that additional APIs or codes have to be implemented either by Amazon, a third-party, or users themselves. In comparison, Google has almost the same data model: buckets “are the basic container [. . .] everything [. . .] must be contained in a bucket”. Objects have two components: “object data” and “metadata” (Google, 2012b). Both S3 and GCS share a lot of technical details in common, such as the data model, file naming, and access to APIs. 3.3.1 File and directory structure. S3 and GCS are not considered the same as a conventional hierarchical file structure. In S3 and GCS, every object is contained in a bucket, pretty much the same as the data structure concept “bag”. Theoretically this data model is simple, and by nature this is a flat structure. The objects can be logically viewed in linear, hierarchical (traditional storage structure) or even more complex ways. In order to mimic traditional files and directories, one can create file names like “photos/2006/123.jpg” or “photos/2006/124.jpg”. These two files are logically stored in the directory “photos/2006”. Our program, S3 and GCS browser tools can display files and directories in this traditional view. 3.3.2 Bucket’s name. A bucket’s name must be unique. The author designed a consistent way to name a bucket using the system’s time stamp. 3.3.3 API. Both S3 and GCS have a simple API for integration. The program’s code for integration with S3 is fewer than 100 lines. Coding and testing time is less than ten hours. 3.3.4 Web interface. S3’s web interface is straightforward and suitable for manual operations. One can create buckets and upload files, but with limitations on uploading directories. In other words, the current “Upload” command (see Figure 1) cannot upload any directory. An “Enhanced Uploader (BETA)” tool resolves this issue, but requires an “enabled Java applet” to run. The GCS manager is the web interface, which is very similar to that of S3.
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3.4 Differences: limitations The author believes that there is a significant difference between S3 and GCS in their approaches to integration with programs. That is, S3 can be integrated with any
Figure 1. Amazon S3 web interface
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program whether the program runs on cloud or on local, while GCS is difficult to use for applications that are not running on GAE. In the above case, the author’s application is required to read and analyze local files so that it can generate file identification information. Therefore, running it on a local machine is the most effective and efficient way. The application integrated with S3 with Amazon AWS SDK wrapped inside. In contrast, Google claims that the GCS API is “experimental, innovative and rapidly improving” (Google, 2012a). Given the same technical environment, the author finds that it is very difficult to integrate with GCS. At present, the author feels that integration with GCS requires additional work and that future code updates can be expected. In the case of implementing the author’s program, integration with S3 is straightforward and it takes a day or two to develop and test the code. This reveals a huge limitation in using GCS as an on-demand cloud storage service. The current GCS implementation is more suitable for programs running on the GAE platform. GAE is a PaaS platform, which is intended to make it easy to write applications without worry about managing the underlying OS, networking, and programming languages. However, this is a huge pitfall for programs that cannot be run on it. It is identified that GAE has at least the following limitations: . It currently supports Java, Python and Go, although Google plans to support more languages in the future. . the database is GQL, which does not have SQL’s join statement (Han, 2011), though Google Cloud SQL can be used. This design has its advantages, but the drawbacks are also obvious. As a result, only a range of applications can be run on GAE, or they have to be modified. 4. Uses of IaaS virtual machine with Amazon EC2 and CloudWatch EC2 is the central piece of Amazon AWS, which can be operated independently or integrated with other AWS services. Multiple articles have mentioned that it allowed a library to rebuild, upgrade, and backup web applications quickly. In comparison, rebuilding and/or upgrading traditional servers and/or applications can be cumbersome and time-consuming. An EC2 instance comes with standardized resources. For example, a small instance has one EC2 compute unit, 1.7 GB memory and 160 GB storage. For a typical library repository, such an instance has enough computing power to handle incoming requests, but may not have enough storage for growing digital collections. The Afghanistan digital collections repository using DSpace (see www.afghandata.org) uses an EC2 small instance, hosting 1,800 titles of 200,000 images in 2010. Since then, an additional 1,200 titles of 150,000 digitized images have been added. As a result, the basic storage of a small EC2 instance is not enough and additional storage must be added. Typical system administration of a server involves regular backup and increasing storage from time to time. The following shows how to perform these operations, which are easy-to-use and can be performed much more quickly and easily than managing a traditional server. 4.1 Increase storage Because CC is by its nature an on-demand service, users can buy just enough resources (e.g. CPU, memory, and storage) for their planned use. Additional resources, such as storage, can be added at a later time when needed. In AWS, one can use “Create
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Volume” to create a new Elastic Block Store (EBS), which can be any reasonable size. A new EBS volume is unformatted block storage. When attached to an EC2 instance, it shows up as a device. Then a file system in a volume can be created and mounted to the instance as storage. This volume can be backed up independently, increased at a later time, detached, and re-attached to another instance if needed, without the creation of any downtime. These operations are straightforward, and very easy to do. This feature is very useful for website and repository systems to avoid downtime. 4.2 Back up instance and creation of a machine image Backups are necessary and critical for recovery and data review. Their primary purpose is to recover data in the case of system failure and data loss. Sometimes users might want to examine and review data from an earlier time for business purposes. Why is backup important? A survey of 2,205 US adults revealed that 54 percent of adults personally have and/or know someone who has lost files. Nineteen percent of men and 30 percent of women do not back up (Seagate, 2012). Backup methods can be full or incremental. A full backup aims to record the state of a server, and is generally completed with system imaging backup software, while an incremental backup is records changes since the last incremental backup. An incremental backup is usually desirable for saving storage space without backing up duplicated files. On a locally managed server, one must use open source or proprietary software to create a full backup. For incremental backup, database data can be dumped and files can be synchronized. Both backups require staff time and computing resources, i.e. the installation, maintenance and operation of backup software and storage spaces, preferably located in a remote facility. As a result, backup can be time-consuming and expensive to run over time. The EC2 web interface provides an easier way. For a full backup, one can use an existing feature to create an image of an existing instance. In AWS this is called Amazon Machine Image (AMI), which fully captures an existing EC2 instance. An AMI can be only accessed by certain users, or can be shared with desired users. For an incremental backup, one can synchronize files with a different EBS. Creating backups is just a few mouse clicks with the AWS web interface, or a program can be developed to create an automatic backup schedule. In order to restore and recover the system, one can re-launch an instance from an existing image in a few seconds (see Figure 2). 4.3 Monitoring a virtual machine using CloudWatch Consistently high CPU usage, low memory, and excessive IO usage are important indicators for server/instance overload, meaning that the current instance struggles to handle IO requests and tasks. If the situation persists, it is a generally good practice to upgrade the server/instance. In the past, the author configured and maintained system monitoring tools and services such as Nagios. They are critical tools for system administration, but this traditional approach requires staff time and resources for maintenance. Amazon CloudWatch is a cloud monitoring service for AWS resources. The basic monitoring service is free of charge and watches EC2’s resources such as CPU utilization and disk read/write. A similar product is Rackspace’s monitoring service. Compared to their traditional counterparts, these monitor services save administrators’ time and resources. To enable the basic monitoring service for an EC2 instance, one can set up alarms for the instance’s health, such as CPU usage, disk
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Figure 2. Create Image in the Amazon EC2 web interface
read/write and network status (see Figure 3). The author has set up two alarms to monitor an EC2 instance running a DSpace repository. One alarm was set up to notify of high CPU usage (e.g. CPU usage is over 50 percent), while the other is used to warn of system failure (e.g. system check failed). 5. Discussion The author has been using CC services at all levels over the past few years. His experience and the above literature review show that CC has advantages: . On-demand – Libraries are no longer required to plan ahead for IT resources. Large-scale computing power and Petabyte storage can be acquired at any time. For example, DuraCloud and the Central Connecticut State University Libraries just purchase required storage as they go, and do not need to plan ahead.
Figure 3. Create An Alarm in Amazon CloudWatch
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Cost-effective – Some of SaaS services are free of charge, while IaaS services are billed by the resources used. Multiple articles show that CC in general is more cost-effective than locally hosted service. Users are encouraged to consider the total costs of ownership. Scalability – Without CC, Mike Nilson would have not been able to run 20 EC2 instances simultaneously for his short 40-hour project (Nielsen, 2012). Without CC, the Institute of Systems Biology would have not been able to analyze cancer data sets in a short time (Google, 2012c). CC opens a new way for scientists to work more effectively. On-demand cloud storage can be acquired at any time. With many services to choose from, libraries are no longer limited to the boundaries of their own IT resources. Convenient – As mentioned above, increasing storage and the creation of a new instance can be completed easily without the creation of downtime. Library users will be happy not to see an e-mail informing them of system downtime. CC providers provide an easy way to monitor costs, as the services are billed by the resources used. The author is able to monitor and control the costs. High availability – Large IT companies have tremendous technical and financial resources. There is occasional downtime, but the availability of their CC services is much higher than that of locally maintained servers. The author’s experience shows that the availability of CC services is very high.
Readers should not forget that security is a major concern. There are a few known incidents of security concerns. For example, in 2011 Dropbox had a four-hour time period during which users could log in with any password (Singel, 2011). Eranki, head of Dropbox Server Engineering, stated that “I think a lot of services (even banks) have serious security problems [. . .] so figure it out if it really is important to you [. . .] before you go and lock down everything” (Eranki, 2012). Not all CC services are created equal, even though they might provide similar services. For example, S3 is better than GCS if a program running locally requires on-demand cloud storage. If you are developing a program solely on GAE, then GCS is obviously a better choice. The EC2 virtual machine comes with useful tools such as the monitoring service and imaging creation tools. This gives it an advantage over other IaaS virtual machine services. For users who are considering CC for individual uses and/or enterprise integration, the author suggests considering the following: . Content – What is the data? How much? Is the data sensitive? How long will you keep the data? For huge amount of data for short-term usage, CC is probably the way to go. For sensitive data, one must follow policies and perform encryption to secure it in the cloud. . Users – Who are the end users? If individual use for daily office routines, consider SaaS-level services. . Skills – Integration of PaaS and IaaS requires programming and database administration skills, although system administration skills have been removed. Technical skills and ongoing support are still required for integration.
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Costs – CC services remove startup costs for investing in physical IT equipment, but there are ongoing bills. Readers should be aware of the total costs. What are the total costs of ownership? Will running CC save money? Security and privacy – Libraries have a role to keep sensitive data safe as circulation records secure. Some technical and legal steps are suggested. Encryption of sensitive data and appropriate clauses in contracts with CC providers are good practices to keep data safe (Moulaison and Corrado, 2011).
6. Summary The article began with NIST’s definition to understand the three levels of CC. The literature review has shown that there are many articles discussing CC in libraries in terms of implications, potential uses, case studies, and costs. The author has analyzed these published articles and has shown how libraries utilize CC services at the three levels (i.e. SaaS, PaaS and IaaS). The author has also found that few studies mentioned integration with S3 and Microsoft Azure cloud storage. No studies provided detailed implementations or mentioned Google’s GCS. This article describes a case study of integrating cloud storage using S3 and GCS. S3 can be integrated with any program whether the program runs on cloud or locally, while GCS is only good for applications running on GAE. The limitations of the current GCS approach make it hard to use for stand-alone cloud storage. The author also discussed virtual machines using EC2 and its related tools for backup, increase storage, and monitoring service. These services make system administration easier as compared to the traditional approach. The advantages of CC are discussed by showing case studies from libraries. The security of data in the cloud is a great concern, and suggestions on this matter are proposed. Finally, some questions are given for readers to consider before using a CC service. References Amazon (2006), “Working with Amazon S3 buckets”, available at: http://docs.amazonwebservices. com/AmazonS3/latest/dev/UsingBucket.html (accessed August 28, 2012). Amazon (2008), “Amazon Web Services: overview of security processes”, available at: http://aws. amazon.com/articles/1697?_encoding¼UTF8&jiveRedirect¼1 (accessed August 20, 2012). Amazon (2012a), “Amazon Simple Storage Service (Amazon S3)”, available at: http://aws. amazon.com/s3/ (accessed August 20, 2012). Amazon (2012b), “Amazon Glacier overview”, available at: http://aws.amazon.com/glacier/ (accessed August 31, 2012). Bagley, C.A. (2011), “Parting the clouds: use of Dropbox by embedded librarians”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Branan, B. (2011), “DuraCloud: a technical overview”, available at: www.slideshare.net/ DuraSpace/dura-cloud-technicaloverview201111finis1 (accessed October 22, 2012). Breeding, M. (2011), “Library discovery services: from the ground to the cloud”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. CILIP Update (2012), “Cloud management system put to test”, CILIP Update, Vol. 11 No. 8, p. 18.
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Coombs, K.A. (2011), “Leveraging OCLC cooperative library data in the cloud via web services”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Davison, J. (2011), “Building push-button respositories in the cloud with DSpace and Amazon web services”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Deeds, L.R., Kisselo-lto, C. and Knox, A.T. (2011), “Ning: fostering conversations in the cloud”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Ditkoff, J. and Young, K. (2011), “Speak up! Using VoiceThread to encourage participation and collaboration in library instruction”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Doelitzscher, F., Sulistio, A., Reich, C., Kuijs, H. and Wolf, D. (2010), “Private cloud for collaboration and e-learning services: from IaaS to SaaS”, Computing, Vol. 91 No. 1, pp. 23-42. Eranki, R. (2012), “Scaling lessons learned at Dropbox, part 1: the security-convenience tradeoff”, available at: http://eranki.tumblr.com/ (accessed October 10, 2012). Galvin, D. and Sun, M. (2012), “Avoiding the death zone: choosing and running a library project in the cloud”, Library Hi Tech, Vol. 30 No. 3, pp. 418-427. Gleason, A.W. (2011), “Not every cloud has a silver lining: using a cloud application may not always be the best solution”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York. NY. Google (2012a), “Google Cloud Storage API overview”, available at: https://developers.google. com/storage/docs/developer-guide (accessed October 10, 2012). Google (2012b), “Google Cloud Storage Java API overview”, available at: https://developers.google. com/appengine/docs/java/googlestorage/overview?hl¼en (accessed October 20, 2012). Google (2012c), “Cancer investigators use Google Compute engine to accelerate life-saving research”, available at: https://cloud.google.com/files/ComputeISBCaseStudy.pdf (accessed October 20, 2012). Han, Y. (2011), “Cloud computing: case studies and total cost of ownership”, Information Technology and Libraries, Vol. 30 No. 4. Iglesias, E. and Meesangnil, W. (2010), “Amazon S3 in digital preservation in a mid-sized academic library: a case study of CCSU ERIS digital archive system”, The Code4Lib Journal, Issue 12, available at: http://journal.code4lib.org/articles/4468 (accessed January 5, 2011). Kroski, E. (2009), “Library Cloud Atlas: a guide to cloud computing and storage”, Library Journal, available at: www.libraryjournal.com/article/CA6695772.html (accessed August 1, 2012). Leonard, A. (2011), “From the cloud, a clear solution: how one academic library uses Google Calendar”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Library of Congress (2009), “Library of Congress and DuraCloud launch pilot program using cloud technologies to test perpetual access to digital content”, available at: www.loc.gov/ today/pr/2009/09-140.html. Mell, P. and Gance, T. (2011), “The NIST definition of cloud computing”, available at: http://csrc. nist.gov/publications/nistpubs/800-145/SP800-145.pdf Milberg, C.I. (2012), “A tale of two systems: a case study on the implementation of two discovery systems at Davidson College”, College and Undergraduate Libraries, Vol. 19 Nos 2-4, pp. 264-277.
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Miller, R.E. (2011), “Integrating Google Forms into reference and instruction”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Mitchell, E. (2010), “Using cloud services for library IT infrastructure”, The Code4lib Journal, Issue 9, available at: http://journal.code4lib.org/articles/2510/ Moulaison, H.L. and Corrado, E.M. (2011), “Perspectives on cloud computing in libraries”, Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Murray, P. (2008), “Long-term preservation storage: OCLC Digital Archive versus Amazon S3”, Disruptive Library Technology Jester, available at: http://dltj.org/article/oclc-digitalarchive-vs-amazon-s3/ Nighswonger, C.R. and Engard, N.C. (2011), “Koha in the cloud”, in Corrado, E.M. and Moulaison, H.L. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman, New York, NY. Nilsen, M. (2012), “How to crawl a quarter billion webpages in 40 hours”, available at: www. michaelnielsen.org/ddi/ (accessed October 20, 2012). Quint, B. (2008), “OCLC introduces high-priced digital archive service”, Information Today, available at: http://newsbreaks.infotoday.com/nbReader.asp?ArticleId¼49018. San Diego Supercomputer Center (2011), “SDSC announces scalable, high-performance data storage cloud”, available at: www.sdsc.edu/News%20Items/PR092211_sdsccloud.html (accessed October 20, 2012). Seagate (2012), “Seagate reinvents backup for your digital life”, available at: www.seagate.com/ about/newsroom/press-releases/backup_plus_launch_master_pr/ (accessed August 22, 2012). Singel, R. (2011), “Dropbox left user accounts unlocked for 4 hours Sunday”, The Wired, available at: www.wired.com/threatlevel/2011/06/dropbox/ (accessed October 20, 2012). Tonjes, C. (2010), “Cloud computing at DCPL”, LITA Cloud Computing Session, available at: www.slideshare.net/ctonjes/chris-tonjes-cloud-computing USC Shoah Foundation (2011), “Institute’s archive now in the Cloud”, Institute News, available at: http://dornsife.usc.edu/vhi/news/3294 (accessed October 10, 2012). Waddington, S., Zhang, J., Knight, G., Hedges, M., Jensen, J. and Downing, R. (2012), “Kindura: repository services for researchers based on hybrid clouds”, Journal of Digital Information, Vol. 13 No. 1, available at: https://journals.tdl.org/jodi/article/viewFile/5877/5887 (accessed August 30, 2012) Further reading DuraCloud (2011), “DuraCloud Dictionary”, available at: www.duracloud.org/duracloud_ dictionary Kincaid, J. (2011), “Dropbox security bug made passwords optional for four hours”, available at: http://techcrunch.com/2011/06/20/dropbox-security-bug-made-passwords-optional-forfour-hours/ (accessed October 10, 2012). Corresponding author Yan Han can be contacted at: [email protected]
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1. Sanjay Sharma, Bharat Sharma. 2016. Optimal selection of application loading on cloud services. International Journal of Production Research 54:21, 6512-6531. [Crossref] 2. Sitalakshmi Venkatraman. 2013. Software Engineering Research Gaps in the Cloud. Journal of Information Technology Research 6:1, 1-19. [Crossref]
IMAGINING LIBRARY 4.0: CREATING A MODEL FOR FUTURE LIBRARIES
The Journal of Academic Librarianship 41 (2015) 786–797
Contents lists available at ScienceDirect
The Journal of Academic Librarianship
Imagining Library 4.0: Creating a Model for Future Libraries Younghee Noh Department of Library and Information Science, Konkuk University, 322 Danwol-Dong, Chungju-Si, Chungcheongbuk-Do 380-701, South Korea
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Article history: Received 13 February 2015 Accepted 28 August 2015 Available online 1 October 2015 Keywords: Library 4.0 Web 4.0 Intelligent library Massive data library Augmented reality Context-awareness
a b s t r a c t Purpose: The purpose of this paper is to suggest a Library 4.0 model based on the concepts of Library 4.0 discussed in the literature as the future of library service. The concepts and model of Library 4.0 can be adapted to fit every different kind of library. Design/Methodology/Approach: For this purpose, first, major reference databases (e.g. Google Scholar, EbscoHost, LISA, etc.) were examined for literature that discusses Web 4.0 and Library 4.0. Second, examples of information technology environments as well as studies and news articles related to information technology were comprehensively collected and analyzed by focusing on those which may influence libraries. Third, examples of cutting-edge information technology applied in libraries were examined and analyzed. Other examples were found of cutting-edge information technologies that have not yet been used in libraries but would be applicable to the next-generation library. Fourth, this study developed a model for next-generation library service provided by Library 4.0 and representative keywords explaining Library 4.0. Findings: First, opinions of scholars tracking the rise of Web 4.0 vary widely, but Web 4.0 features commonly suggested by previous researchers are: reading, writing, and executing simultaneously, intelligence-based agents, connected web, ubiquitous web, intelligence connections, and intelligence-based web. Secondly, this study determined the features of Library 4.0 as: intelligence-based, massive data, augmented reality, context aware, cutting-edge displays, and infinite creative space. Third, in this context, the keywords that best explain Library 4.0 are: Intelligent, Makerspace, Context-Aware Technology, Open Source, Big Data, Cloud Service, Augmented Reality, State-of-the-art Display, and Librarian 4.0. Originality/Value: Discussions about Web 4.0 have begun, but little has been written about Library 4.0. This study is significant for deriving keywords for Library 4.0 and presenting the development direction of Library 4.0. In the future, research on Library 4.0 can actively proceed from this starting point. © 2015 Elsevier Inc. All rights reserved.
INTRODUCTION The LIS field has seen discussions of Library 3.0 for the past 10 years. The development stages of various library iterations have been continually researched by scholars and analyzed by field librarians as new digital technologies allow for large-scale changes in a short amount of time. Libraries, by nature, are very similar to living organisms in that they are influenced by external pressures to constantly evolve, including, in this case, changing information technology environments and a greater reliance on web-based services. The age is fast approaching when technology and humanity will merge and become one (Rohrbeck, Battistella, & Huizingh, 2012). Passive entertainment such as television in its current form represents the 1.0 age, while Web 2.0 represents an age of content created by users, such as blogs and podcasts (Kirschner & Muller, 1987). Web 3.0, then, is the ongoing era of users jumping into media, using virtual worlds and becoming more active. In the future, Web 4.0 will be when
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http://dx.doi.org/10.1016/j.acalib.2015.08.020 0099-1333/© 2015 Elsevier Inc. All rights reserved.
humans are upgraded with technological extensions, always connected to the internet (“always-on”) (Farber, 2007). That era has already started for the younger generations alive today, who already communicate with the web in the same way that they would talk to their friends (Kirschner & Muller, 1987). The online space and the physical space are not as differentiated for them as for generations past. Web 3.0 represents data and analysis filtered through artificial intelligence, while Web 4.0 technology will become one with users' lives (Callari, 2009). Godin (2007) lists three conditions for constructing Web 4.0: ubiquity, identity, and connection. “Ubiquity” refers to the lines between offline and online life becoming blurred, with users connected to Web 4.0 at any time and place. “Identity” means specific protocols will exist to determine efficiently who the users are, what they are doing, and what kinds of things they need. “Connection” means a continuously connected network of users. Godin predicts that, once Web 4.0 is constructed, unwanted information like spam emails will disappear and only information needed by users will be provided because, unlike versions of the web in the past where users wander from place to place in a sea of information when searching, Web 4.0 will only provide information suitable for users by integrating all the
Y. Noh / The Journal of Academic Librarianship 41 (2015) 786–797
known data about their identity. For example, customers passing by a particular store will be identified, and personalized advertising messages for each person will display for them as they pass. Library 3.0 or 4.0 will not only reflect the changing nature of the web as described above, but it will also feature new attributes based on the uniqueness of libraries. Noh (2010) said Library 3.0 will combine the concepts of a social semantic digital library, linked library, and mobile library, while Kruk, Decker, et al. (2007), Kruk, Woroniecki, Gzella, Dabrowski, & McDaniel (2007) and Alotaibi (2010) emphasize the social semantic library as a 3.0 library. In light of the services presently provided by libraries, we are already living in the era of Library 3.0, and it seems appropriate to begin a discussion of Library 4.0. While substantial time has passed since the discussion of Web 4.0 started, it is difficult to find studies suggesting models for Web 4.0 and predicting its features. However, considering the discussions of Web 4.0 made by researchers, some concepts have already been introduced to libraries, beginning to constitute Library 4.0, and other concepts are still being actively discussed in advance of being applied to libraries. However, these discussions are fragmentary and not embracing Library 4.0 as a whole. Therefore it is necessary at this time to start a discussion on Library 4.0 to predict the direction of and strategies for development of future libraries so that future librarians can play a leading role in responding to the era of Web 4.0. Technology keeps making great leaps forward, meaning that today most people have access to devices that were only the stuff of science fiction in the films of ten to thirty years ago. It is imperative that library development stays abreast of these fast-moving trends. Accordingly, this paper suggests a Library 4.0 model based on the concepts of Library 4.0 discussed in the literature and the aspects that have already been applied in some contexts. The concepts and model of Library 4.0 can be adapted to fit every different kind of library. RELATED WORKS ON LIBRARY 4.0 Many scholars have suggested developmental directions for future libraries. In particular, when a new concept or technology appears and massively influences society at that specific point in time, researchers present forecasts for how it will influence libraries and how libraries will develop thereafter. In this study for suggesting a library model, the first step was naturally an attempt to analyze all of the available research. However, it was discovered that there are few studies available specifically on the topic of Library 4.0. Thus, considering that Library 4.0 is a form of next-generation digital library, this study focuses on examining the papers suggesting models of next-generation digital libraries. First, there are studies classifying and examining the features of digital libraries from their first generation to the present and suggesting development directions for the next-generation libraries. Greenstein and Thorin (2002) focused on the experiences of premier research libraries in the USA and comprehensively discussed the essential challenges faced by digital libraries as well as how cultural, legal, and financial support would influence the history and development directions of digital libraries in the future. Almost 5 years before Greenstein, Mukaiyama (1997) argued that digital libraries will hold a central place in the 21st century and technologies making up the next-generation digital libraries will be threesystem architecture (integrated messaging system, electronic agents, multimedia database, and application system), individual technologies (digitized literature, smart search engines, SDI agents, concept-based search, hypermedia search, and concept-based video search using 3D visualization), and integration technology (for instance, content recording structures). These technologies have already been applied to libraries. Kroski (2009) listed the defining features of present-iteration digital libraries as mobile (new services and basic technologies, mobile content
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and new transmission formats, and mobile apps), social (social and library websites, attractive user experience, and cooperation with community businesses), and open (open source applications and open content). Kroski also predicted that essential areas of next-generation digital libraries would be the semantic web, grouping, cloud computing, life streaming (online recording of daily life by collecting blog comments or online photos and directly shooting videos), and filtering. Therefore, the elements Kroski forecasted for next-generation digital libraries (NGDLs) roughly match those ascribed to Web 4.0 to some degree. Breeding (2011) argued that plans in preparation for future libraries are essential for fully utilizing new technologies as soon as possible to avoid obsolescence. As he pointed out, up to now, changes in libraries have been in terms of formats (digitalization and adoption of various multimedia) and affluent convergence (lack of boundaries between equipment and content formats). RFID systems, which presently allow automatic and simple handling of physical materials, may lose their value. However, he was not able to forecast which technology would play a significant role in the library's future. Piper (2013), in his paper regarding the future digital nature of libraries, referred to a project conducted by HathiTrust (hathitrust.org) and DPLA (Digital Public Library of America) as a model which may become a guideline for constructing massive libraries in the next 15 years. The project will be conducted based on a shared system, metadata, and digitalized contents. The project is very similar to Google's digital books endeavor (GoogleBooks) except without the commercialization aspect. The process is the same: 1) providing search tools for the virtual catalogs available, 2) expanding the scope for all books written in all languages, and 3) user-based systems for helping users find new books. Cooperation between organizations on such a massive scale has not been possible before. McGettigan (2013) introduced examples of construction of NGDLs and information services, hybrid libraries combining traditional libraries and virtual ones providing virtual reference service, personalized OPACs, 24-h service, and downloadable media. The revolutionary service spirit of next-generation digital libraries is based around the ideals of space for free community networking, technological resources provided free of charge, connections with the local economy, a sense of belonging to community, and promoting a high level of trust in the local community. Other such efforts have been made by public libraries, notably the Chattanooga Public Library and the Willingboro Public Library. The ALA has provided examples of applying cutting-edge technologies to library services since 2009, and in 2013 evaluated the most innovative ones among them as mobile internet, cloud sourcing, open source development, and cost effective online education (ALA, 2013a, 2013b, 2013c). The ALA also selected five outstanding cases of institutions applying these revolutionary technologies to libraries. Among those cases, Corcoran Library (Boston College High School Library in Massachusetts) allowed all students to access the library's online resources through their mobile sites and developed applications which become optimized for mobile search. The library announced that it will develop archives and a virtual reality tour available through QR code (ALA, 2013a, 2013b, 2013c). In 2014, excellent examples selected by the ALA were: 1) “Creative Solution”, a digital sign board system, 2) “Me Card Technology” which allows users to access all the libraries connected through one card, 3) a system under which the users and the department of archives can upload open stories of new library construction through photo and video streams, and 4) simple video creation systems (ALA, 2014). In addition, there are many studies discussing innovative changes in the contents of books in NGDLs. Among them, Crane et al. (2006) conducted research modeling of NGDLs and discussed groundbreaking changes in the content of books. The limit of NGDLs, he concluded, is based on existing print versions. He also described necessary features of future digital collections as: sophisticated screen design, voluntary learning, and real-time community participation. He asserted that,
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based on books having those features, customized services and personalized services will become available. After analyzing the previous research, it is apparent that the keywords and concepts which should be included in Library 4.0 are semantic web, cloud computing, life string (Kroski, 2009), virtual reality of the open source library (ALA, 2014), and virtual library (Chow et al., 2010). In this section, the literature of Library 4.0 based on Web 4.0 was examined. The concepts and development process of Web 4.0 and Library 4.0 will be discussed further in later sections.
RESEARCH QUESTIONS This paper intends to analyze trends of information technology as well as their social and cultural environments, comprehensively analyze research papers discussing Web 4.0 or Library 4.0, and forecast the future design of Library 4.0. A secondary goal is to identify keywords explaining Library 4.0 and the services it will provide. The research questions raised by this process are as follows: RQ 1: How advanced are current discussions of Web 4.0? RQ 2: What do researchers suggest will be key features of Web 4.0? RQ 3: What will be major keywords explaining Library 4.0?
RESEARCH PROCESS AND METHOD DEVELOPMENT OF WEB 4.0 MODEL RESEARCH METHOD DEVELOPMENT OF WEB 4.0 The aim of this paper is to discuss possible development directions for Library 4.0, using the research content that follows (Fig. 1). First, all literature available on major reference databases (e.g. Google Scholar, EbscoHost, LISA, Korean databases, etc.) that discusses Web 4.0 and Library 4.0 was collected. The keywords used for searching were Web 1.0, Web 2.0, Web 3.0, Web 4.0, Library 1.0 through Library 4.0, NGDLs (next generation digital library), and many individual kinds of cutting-edge technologies. Literature was then categorized by each web or Library iteration number, and by cutting-edge technologies. Second, examples of information technology environments as well as studies and news articles related to information technology were collected and analyzed by focusing on those which may influence libraries. Third, cutting-edge information technology applied to libraries were examined and analyzed. Also, some cutting-edge information technologies which have not yet been used in libraries but would be applicable to the next-generation library were discovered. Fourth, this paper developed a model for next-generation library service consisting of Library 4.0 and representative keywords explaining Library 4.0 (Fig. 5).
According to Berners-Lee (2006), Web 1.0, as the first generation of the internet, was not just read-only content but also a recognition system. Web 1.0 started as a sort of information space informing people of data for business with very limited interactions between users and almost no content creation. Information search and information consumption were the main activities available at that time. Hassanzadeh and Keyvanpour (2011) said that Web 3.0, or the semantic web, reduces the amount of time and decision-making required from users with machine-readable content that allows the system to do most of the work. Web 3.0 includes two platforms: a semantic technological environment and a social computing environment. The semantic web represents open standards, and social computing allows cooperation between human users and machines so that massive social communities can be effectively organized (Norasak, 2008). Web 4.0 will be a “read, write, execute, and concur web” where intelligent interaction is available. Web 4.0 will be a more symbiotic web where human users and machines have more personal interactions. Fowler and Rodd (2013) said that “ultra-intelligent electronic agents” will be the defining feature of Web 4.0, and they also summarized characteristics of Web 1.0 through Web 2.0 and insisted that the
Fig. 1. Research method and process.
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development between generations has come faster and faster, with each generation having a shorter lifespan. In other words, one of the most prominent features of Web 1.0 were search engines, such as Yahoo in the early 1990s. Web 2.0's defining characteristic was social media, such as Wikipedia, a cooperation project, and SNS (social networking sites) such as Facebook and Twitter. Web 3.0 is most commonly known for the “3D Web” based on fast computer processing and the rapid development of network and storage (Burrus, 2013). Burrus (2013) argued that super-smart electronic agents embedded with small cameras can identify the user and may be able to manage all aspects of daily life for the user from the moment of waking up in the morning, just like a secretary or friend. However, just as we cannot predict the exact concepts for next-generation smart phones until they are developed and unveiled, so too is it impossible to know the exact image of Web 4.0, as it will look in the future. He also added that, just as we predict wearable smart phone technology may become popular soon, Web 4.0 is expected to have a similar performance capacity. Burrus also summarized succinctly that Web 1.0 was for search, Web 2.0 was for social media, Web 3.0 is for the 3D Web, and Web 4.0 will open the era of smart agents. Kang and Yong (2007) wrote that Web 1.0 brought about the concept of information connection, while Web 2.0 is characterized by the concept of allowing creation, storage, evaluation, and sharing of information through users' active participation. Web 3.0, the semantic web, is a web where data and knowledge are connected based on intrinsic meanings rather than just links of information, and Web 4.0 will represent an upgraded, higher level intelligence on the part of technology, and a ubiquitous web based on a web operation system under which everything is connected, such as an “Internet of Things”. Chauhan (2009) said that Web 4.0 will have a structure which not only includes all services and functions, but is supported by artificial intelligence, i.e., the web will be able to analyze information, discuss with other people interested in the same areas, and create new ideas or theories. Web 4.0 will be able to let researchers know information suitable to their research and discussions carried out through the internet or mobile devices even when they are in a different place. Skryabina (2010) compared the features of Web 1.0 through Web 4.0: Web 1.0 represents an era of availability of information delivery. Web 2.0 represents an era allowing interaction between users. Web 3.0 is the era when direct cooperation is available for a new start. Web 4.0 represents a generation-changing concept to make society a better place through innovation.
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Aghaei, Nematbakhsh, and Farsanim (2012) discussed the development of Web 1.0 through Web 4.0, and they defined them as follows: Web 1.0 as a web of information connection, Web 2.0 as a web connecting people, Web 3.0 as a web connecting knowledge, and Web 4.0 as a web connecting intelligence. Patel (2013) introduced and compared the features of each generation of Web 1.0 through Web 5.0 and tried to analytically suggest development directions for the web. He suggested a classification of web generations: Web 1.0 as an awareness web exclusively for reading; Web 2.0 as human-centered participation web for reading and writing; Web 3.0 as a knowledge connection web for reading, writing, and executing; Web 4.0 as an ultra smart electronic agent for reading, writing, and executing; and Web 5.0 as a web appealing to feelings and emotions. Sharma (2012) described Web 1.0 as a one-dimensional web; Web 2.0 as a social web; Web 3.0 as a semantic web; and Web 4.0 as an intelligent web. From the point of view of data volume and search ability, he explained the webs as described in the following diagram. As we see in Fig. 2, while the data volume rapidly increased until Web 2.0, afterward it began to decrease. Search support functions developed from keyword search to tagging, natural language search, semantic search, and inference; inference search became available when the world entered into the era of Web 4.0. Web 4.0, as described from the results of the research mentioned above, is: reading, writing, and simultaneous execution (Aghaei et al., 2012; Patel, 2013), ultra-intelligent electronic agents (Fowler & Rodd, 2013), an Internet of Things (Kang & Yong, 2007), an intelligent web (Chauhan, 2009; Sharma, 2012), and an intelligent connection web (Aghaei et al., 2012). Based on the various research (Patel, 2013), the development of web versions can be organized and characterized as in Table 1. The next chapter will conduct a comprehensive analysis of these discussions and suggest a model for Web 4.0. WEB 4.0 MODEL As is shown above, many researchers have begun a discussion of Web 4.0, but its exact definition has not yet been decided. However, based on the views of scholars, various keywords and features are usually agreed upon. For example, Godin (2007) suggested three conditions for Web 4.0: first, “ubiquity,” meaning ubiquitous computing connecting with Web 4.0 at any time and place; second, “identity,”
Fig. 2. Changes of data volume and search ability based on development stages of the web (resource: http://www.scribd.com/doc/99678417/Web2-0-Web3-0-Web4-0).
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Table 1 Web version development. Web 1.0
Web 2.0
1996 The web Tim Berners Lee Read only web Information sharing Millions of users Echo system Connect information Brain and eyes (=information) The hypertext/CGI web. (the basic) Pushed web, text/graphics based flash
2006 The social web Tim O'Reilly Read and write web Interaction Billions of users Participation Connect people Brain, eyes, ears, voice, and heart (=passion) The community web (for people: apps/sites) Two-way web pages, wikis, videos, pod casts, shading, personal publishing, 2D portals
Companies publish content that people consume (e.g. CNN)
Search engines retrieve macro contents. Search is very fast but many times results are inaccurate or overwhelming
Web 3.0
2016 The semantic web Sir Tim Berners Lee Read, write and execute web Immersion Trillions of users Understanding self Connect knowledge Brain, eyes, ears, voice, heart, arms, and legs (=freedom) The semantic web (for machines) 3D portals, avatar representation, interoperable profits, multi-user virtual environment (MUVEs), integrated games, education and business, all media flows in and out of virtual web worlds People publish content that other people can consume, People build applications that people can interact with, companies build platforms that let people publish content for companies build platforms that let people publish services by other people (e.g. Flickr, YouTube, Adsense, Wikipedia, Blogger, leveraging the associations between people or special content MySpace, RSS, Digg) (e.g. Facebook, Google Maps, My Yahoo!) Search engines retrieve tags with micro contents (Furl even Search engines will retrieve micro content texts which were retrieves tags with macro contents). The process of tagging is tagged automatically. This implies translating billions of Web 1.0 manual, tedious, and covers negligible percents of the WWW. macro contents into micro contents. The result could be more Web 2.0 tags everything: pictures, links, events, news, blogs, precise search because tagging can solve part of the ambiguity audio, video, and so on. Google Base even retrieves micro that homonyms and synonyms introduce into the process of content texts. search. Undefined. AI and the web learning what you want and 2 way communication through social networking, blogging, wikis, tagging, user generated content, and video. delivering you a personalized web experience.
Static content, one way publishing of content without any real interaction between readers or publishers or each other The web in the beginning when it was first New advances that allow a much more sophisticated user developing interaction with web pages — citizen journalism, social networks and Wikis Personal web sites Blogs Content management system
Wikis, Wikipedia
AltaVista, Google Citeseer, Project Gutenberg Message boards
Google personalized, DumpFind, Hakia Google Scholar, Book Search Community portals
Buddy Lists, Address book
Online social networks
meaning personalized services to be provided by identifying the context of users; and, third, “connection,” meaning consistent connection with other users. He referred to Apple's iPhone as a service model similar to Web 4.0. In addition, major keywords shown to be related to Web 4.0 in the terminology cloud are Convergence, Remixability, Standardization, Participation, Usability, Economy, and Design. However, we also can see that the concepts for Web 1.0 through Web 3.0 like Semantic, Open APIs, AJAX, CSS, RSS, and Social Software also appear (Fig. 3). As described above, representative features of Web 4.0 are referred to as a symbiosis web, reading, writing, and executing simultaneously, web OS, middleware, and a massive web allowing intelligence interaction just like a human brain. While a clear definition of Web 4.0 has not yet been agreed upon by researchers, we can see that it will be a web using artificial intelligence. In this chapter, I would like to examine essential concepts of Web 4.0. SYMBIOTIC WEB Notwithstanding an accurate definition, many people refer to Web 4.0 as a symbiotic web, due to the interaction between human users and machine components as resembling a symbiotic relationship. Web 4.0 technology will have a stronger interface than our current usercontrolled models so that the machine becomes able to make decisions and properly execute them based on the content it reads itself (Hemnath, 2010). Patel (2013) also discussed the likelihood of Web 4.0 involving sophisticated user interfaces for human–technology interactions to become more symbiotic. Aghaei et al. (2012), too, wrote
Thought to be the future — where the web is more interactive with users, leading to a kind of artificial intelligence Semantic blogs: SemiBlog, Haystack, Semblog, Structured Blogging Semantic wikis: Semantic MediaWiki, SemperWiki, Platypus, dbpedia, Rhizome Semantic search: SWSE, Swoogle, Intellidimension Semantic digital libraries: JeromDl, BRICKS, Longwell Semantic forums and community portals: SIOC, OpenLink DataSpaces Semantic social networks: FOAF, PeopleAggregator Semantic social information spaces: Nepomuk, Gnowsis
about Web 4.0 as a symbiotic web where humans and machines can cooperate and interact with each other. WEB OS One of the early mentions of the concept of Web 4.0 appeared in the international symposium of “Semantic Technology” held in San Jose, California in May 2007. At this symposium, Web 4.0 was defined as upgraded to a higher state of intelligence and a ubiquitous system based on a web operation system under which everything is connected, dubbed an “Internet of Things” (Kang & Yong, 2007). READING, WRITING, AND EXECUTING SIMULTANEOUSLY Marcus (2008) said Web 4.0, with the ability to read, write, and execute simultaneously, will provide global transparency, governance
Fig. 3. Web 4.0 keywords (resource: http://www.brainiac.in).
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structure, distribution, participation, and cooperation to major aspects of society such as industry, politics, and local communities in online networks to maximize participation. In other words, Web 4.0 will ensure global transparency through users' simultaneous participation online. MASSIVE WEB Web 4.0 has also been described as a kind of middleware functioning between software and operating systems (Callari, 2009) and a massive web of highly intelligent interactions much like a human brain (Farber, 2007). INTELLIGENT WEB Many scholars explain Web 4.0 as a kind of intelligent web. Sharma (2012) wrote that Web 4.0 is an intelligent web, able to make inference searches, i.e., Web 4.0 uses artificial intelligence to make a decision, using inference and searched content. This decision will be made based on the system learning over time how we live and what we want. Patel (2013) also defined Web 4.0 as an ultra intelligent electronic agent, symbiotic, ubiquitous, and, in particular, a machine which will be developed up to the level of a human brain, with advanced nanotechnology and human interaction interfaces. Accordingly, the machine in the era of Web 4.0 will be so smart that it will be able read the web content, make and execute decisions on websites, and have a more or less order-oriented interface (Jenkins, 2011). Fowler and Rodd (2013) and Burrus (2013) agreed that “ultra-intelligent electronic agents” were key features of Web 4.0, where the web itself will analyze information, discuss it with interested people, and create new ideas or theories. In this way, Web 4.0 will be able to provide information suitable to ongoing research or discussions to researchers through internet or mobile devices even though they are in different places (Chauhan, 2009). LIBRARY 4.0 MODEL DEVELOPMENT OF LIBRARY VERSIONS Library 1.0 is associated with Web 1.0 in the same way Library 2.0, 3.0, and 4.0 are linked to their corresponding versions of the web. A huge body of research discusses library versions in the field of Library and Information Science. The term “Library 1.0” began to be used for comparison when the term “Library 2.0” was introduced by Michel Casey. Library 2.0 refers to the application of Web 2.0 tools to library services. Library 2.0 is generally perceived as the application of the interactive, collaborative, and multimedia web-based technologies to library services and collections (Maness, 2006). Farkas (2005) explains that Library 2.0 is about allowing user participation through writing reviews and tagging in the catalog and making users' voices heard through blogs and wikis. She insisted that the Library 2.0 makes the library human, ubiquitous, and user-centered. Library 2.0 is a transition within the library world in which programs and services are delivered to the users through new and innovative methods (Cho, 2012; Sanzo, 2008). Cho (2012) said that the principles of Library 2.0 are “entirely” usercentered and that they facilitate seamless collaboration between the users themselves to create community content using new communication technologies. The Library 2.0 is the library which is everywhere (Casey, 2007; Chad & Miller, 2005; Stephens, 2005). The library has no barriers, information resources managed by Library 2.0 are readily available, and barriers to use them are minimized (Chad & Miller, 2005; Stephens, 2007). Library 2.0 invites and facilitates the culture of participation (wikis, blogs, RSS and social bookmarking systems facilitate), drawing on the perspectives and contributions of staff, technology partners, and the wider user community (Miller, 2006; Chad & Miller, 2005; Miller, 2005; Stephens, 2007). Library 2.0 uses flexible best of breed systems, requires a new relationship between libraries and a wide range of
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partners in which all parties together push the limits of what is possible while ensuring that core services continue to operate reliably (Chad & Miller, 2005; Crawford, 2006). Belling et al. (2011) explain that the term Library 3.0 refers to the use of emerging technologies such as the semantic web, cloud computing, mobile devices and established tools like federated search systems, to facilitate the development, organization and sharing of user-generated content through seamless collaboration between users, experts and librarians. Kwanya, Stilwell, and Underwood (2013) defined Library 3.0 as intelligent, organized, a federated network of information pathways, apomediated, and “my library”. It is generally accepted that Library 1.0 represents the conservative traditional library in which the users are passive. Library 2.0 represents a major departure from the conservative library service model and emphasizes the participation of the users to the extent that the librarians are eclipsed. Library 3.0 seems to be a hybrid between the 1.0 and 2.0 models and reasserts the librarians in the information value chain as mediators. The following Table 2 is a comparison of the library versions derived through the analysis of concept definitions and research papers on Library 1.0, Library 2.0, and Library 3.0. EXAMPLES OF APPLYING CUTTING-EDGE TECHNOLOGIES TO LIBRARIES This paper is likely to become the starting point for discussions of Library 4.0. As seen in previous research, many scholars have discussed the development stages of libraries. In other words, in the initial stage, researchers focused on organizing the contents of libraries to be more next-generation oriented, while later research focused on designing futuristic digital libraries connecting with external trends such as Twitter and Facebook. In particular, discussions of Library 3.0 seem to focus on the semantic concept. Oh and Won (2007) defined a digital library as a SSDL (Social Semantic Digital Library), which consists of an ontological system and actively supports the participation and cooperation of users while possessing the essential requirements and architecture models required for the digital library. They divided those requirements into functional requirements and structural requirements. Functional ones include providing significance-based services, providing various points of entry, support of group intelligent activities, and efficient management of information resources. Structural requirements are modularity, significance-based networking, and protection of resources and copyrights. Alotaibi (2010) analyzed the development stages of libraries, discussed the concept of the social semantic digital library, and researched the integrity of social and collective aspects of libraries. Kruk and others conducted studies on SSDL and tried to identify the true nature of SSDL (Kruk, Decker, et al., 2007; Kruk, Woroniecki, et al., 2007). In this study, the researchers analyzed how the semantic web and social networking technologies can support the improvement of digital library services. In addition, through suggesting SSDL structure, Kruk et al. tried to describe various services based on those technologies. Discussing the concept and true nature of Web 3.0, Noh (2010) wrote that the discussions of those concepts have been conducted by a number of scholars and on-site experts in recent years, who analyzed various discussions of Library 3.0, and organized the various proposed concepts for it. Based on these, she suggested models of Library 3.0 services, and the key concepts are: 1) a social semantic digital library where “genuine knowledge sharing and cooperation” is available thanks to the application of semantic web technology, which makes machine data processing and social networking services available to electronic libraries, 2) linked libraries where the library resources become linked data and connect with other libraries all around the world, and 3) ubiquitous mobile libraries with RFID and mobile technology. However, Library 4.0 must include not only software-based approaches but also technological environment development such as makerspace, Google Glass, context aware technology, digitalization of contents, big data, cloud computing, and augmented reality.
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1990–2005 Single echo, one-sided MARC, HTML
Publication Library-centric production, Accumulation centered Read Human Closed, centralized, Librarian-centric PC MARC Metadata Keywords Providing information
PC, Mobile MARCXML, MODS DOI identification system XML/RDF technology Corresponding terminal Knowledge structure
Information consumption Information users Information powers
Read, write Human Popularized, centralized, user-centric
Read, write, execute Human, machine Decentralization (screening only the information required) Dispersion of power between users PC, Mobile, iPad, accessory like a watch, etc. FRBR Ontology The object of semantic structure
Library 3.0 Library 2.0
2006–2010 Bi-directional, public RSS, WIKI, blog, Ajax, Flikr, tagging, podcast, bookmark, Mash-up, toolbar etc.
Library 1.0
CONCEPTS AND KEYWORDS OF LIBRARY 4.0
Time period Interaction Related technologies
Table 2 Library versions
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Chauhan (2009) wrote that the web itself is a kind of library, and it can be classified into different parts based on function: the “learning web” and the “Spamming or Trashing web.” The learning web is a “massive open virtual library” while the “Spamming or Trashing web” includes entertainment. Libraries will be able to survive only by cooperating with various professional academic networks in the era of Web 4.0. He also said that the form of that cooperation will become Library 4.0, and it will be constructed in a virtual library environment where all the services are provided in virtual space. While I attempted to complete a broad search across the literature, information on Library 4.0 was scarce except for those studies described above. However, Library 4.0, as an organic system, must have features closely influenced by changes in the external environment and has almost every feature of Web 4.0. Physical libraries will accept the features of Library 4.0 for its spatial changes. Of course, the concepts of Library 3.0–social semantic digital library, linked library, and ubiquitous mobile library–will be included in Library 4.0 as well. In this context, based on analysis of the information environment and information technology, analysis of previous research and examples, and the development directions of next-generation digital libraries as well as analysis of the social and cultural environment, I would like to suggest the concepts and keywords of Library 4.0. INTELLIGENT LIBRARY From the discussions of Web 4.0, many scholars agree that Library 4.0, as a future library, will become an intelligent library where not only inference and research are available, but the system will analyze information by itself and discuss findings with users like a colleague (Chauhan, 2009; Jenkins, 2011; Aghaei et al., 2012; Sharma, 2012; Fowler & Rodd, 2013). In other words, we can assume that Library 4.0 will bear many similarities to Web 4.0 and incorporate many of the same concepts and technologies. Through that logic, we can imagine an environment that fuses platforms, services, and large amounts of content (massive web), a library that allows librarians, users, and machines to coexist (symbiotic web), technology that allows humans and machines to read, write, execute, and concur at the same time (read–write–execution–concurrency web), and a library that thinks, makes decisions, and provides library services using reasoning (intelligent library). Kwanya et al. (2013) concluded that Library 2.0 pushed the role of the librarian into the background with advances in the search capabilities of technology. They emerge more significant, however, in Library 3.0 where they act as “apomediaries,” helping users locate, access, and use the most accurate and reliable information in different formats from many sources. They also point out that Library 3.0 addresses some of the issues of the reliability of user-created content in Library 2.0 by providing more tools and content to organize the often-chaotic information environment Library 2.0 created. Library 3.0, they ultimately argue, is intelligent and personalizable to a point where it is almost a living organism, sustained by the engagement of users, librarians, and experts. MASSIVE DATA LIBRARY The amount of data and services to be managed by future libraries will be massive enough to transform them into Massive Data Libraries. The concepts and services of big data, cloud service, and open source contents appeared thanks to the scale of accumulated data, expansion of services, and increased availability due to the open-source format. Big data is a massively sized data set which cannot be collected, stored, managed, or analyzed by ordinary database software devices because it stretches those devices beyond the limits of their abilities (Manyika et al., 2011). Big data is cost saving and improves decisionmaking by enhancing insight through massive volume, speed, and innovative information processing (Lehong & Laney, 2013). These big data
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systems may be utilized for improving the information services of libraries, i.e., analysis results of big data can be utilized for planning information service suitable for users, and, based on that result, information resources for each topic, user, and issue may be developed and customized (Lee, 2013). Big data, just like the semantic web and linked data, show human interaction, communication flow, and relationships among data. While Big data is frequently discussed with the concept of cloud computing, it must be considered separately as a massive collection of data available to users whenever they need it through an internet connection and an internet-based computer network. On the other hand, cloud computing is required technology to make Big data available and involves lending cyber-storage space so that users can easily access information technology resources such as servers, storage, applications, and software which exist in shared pools by using the internet at the moment of such a need through various client devices. Although they are linked, it is important to understand and differentiate the concepts of big data and cloud computing (Choi & Woo, 2012). Many methods allow cloud computing to be applied to library services: integrated management of library resources in web-based distributed surroundings, remote access to resources and services, resource sharing among libraries, construction of comprehensive service systems, and cooperation among libraries (Kim, 2012). In addition, the concept of cloud computing is applicable to a cloud collection where digital conservation and conservation of physical, printed copies can play a complementary role. This framework creates new value by jointly preserving and utilizing books, which are preserved through both digital and physical forms (Cho, 2012). Meanwhile, in their research on modeling the next-generation digital library (NGDLs), Crane et al. (2006) described innovative changes on the part of book content and gave an example of open source environment through real-time community participation and distribution contribution. In the massive web, the users create and share knowledge as group intelligence and will expand open source contents. Users reading digital books do not simply receive searched data in the form of PDFs but begin to use all the available data through dynamic connection. The following are features of books in a NGDLs: ♦ Both certain data and its related data are shown on the screen, and these data are created by users or professionals who are connected dynamically in real-time. ♦ Users' individual needs are automatically analyzed, and digital books will be ceaselessly organized to fit those needs. ♦ Classification and data mining, mechanical learning, significant connection between concepts and the literature, and multi-language services (automatic translation) will be automated ♦ Higher quality of book construction, classification, comments, and connections with related papers will be maintained by user participation. ♦ NGDLs will provide personalized and user-oriented services, much more than current iterations.
Digital books are dynamic and connections among concepts and to other works will be perfect through automation or group intelligence. These processes will be carried out with the aim to satisfy the personal demands of users. AUGMENTED REALITY LIBRARY Augmented reality is a technology that shows virtual elements overlaid atop real world displays (Azuma et al., 2001). It is also known as Mixed Reality (MR) because it shows real-world elements combined with virtual ones for supplementary information. The system is available for real-time interactions in 3D spaces so that limitless information can be brought into reality and utilized at the desired time and place (Dunston, 2008). Augmented reality technology is roughly classified
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into location-based augmented reality, for marking information where the user is looking using directions created by other users, image recognition augmented reality for adjusting available content based on visual cues, including camera position, and image tracking augmented reality, which tracks camera location for each frame and then shows suitable content for that frame (Hah, Kim, & Kim, 2011). Hah et al. (2011) applied augmented reality technology in suggesting a system where the information and location of books users are seeking in the library are indicated when they search the catalog and then guides the user to that location. This method for realizing a technology and design using augmented reality for greater user convenience may be applied more extensively in the library, perhaps used for providing: book information and evaluation information of augmented reality to real books, augmented reality information to the building or other features of the library, and augmented reality information to the interior facilities of library. CONTEXT AWARE LIBRARY The next generation of digital library embraces the notion of the ubiquitous library, and context-awareness is one of the emerging technologies necessary for its implementation. Context-aware computing technology is a system designed to search and provide the services that users require in their current situation by analyzing and identifying the available contextual information (the current situation of the user) such as the user's current location, time, people and devices in the vicinity, and the user's behavior and inputted data (Noh, 2013). A service that is context aware uses certain conditions such as the location and environment of users to better respond to user needs. The system combines user-entered information as well as situational data to provide users suitable results. Context aware services have been classified into security service, convenience service, environmental comfort service, entertainment service, information service, and community service (Song, Cho, & Cho, 2008). Context aware services applicable to libraries are book status information (book location checks and guidance service using augmented reality technology, checks and guidance service for books being moved or returned), book content information, My Library management service, library internal information, providing and lending electronic books, and connection with relevant agencies (Lee, 2013). Noh (2013) wrote, in an example of an application of context aware technology to the library, that the library may recognize the user and provide customized service to both new users and existing users. In addition, it can provide information suitable to the circumstances of users, context aware reference and book lending service, and identify the user in an emergency by recognizing their behavior, route, and temperature. Besides this, as an environmental comfort service, temperature, humidity, and lighting can be adjusted for different users, books, and equipment (Song et al., 2008; Noh, 2010). However, it seems that there have not been adequate research on context aware services available in the library, and it is necessary to conduct more study into applying context aware systems to libraries before a broadly applicable system and guidelines thereof can be put in place. CUTTING-EDGE RECOGNITION CAPABILITY Library 4.0 will make it possible to realize a cutting-edge display environment equipped with recognition capability. The technologies and products making that environment possible have already been launched, and applying them well to the developing NGDLs will be the key to success. Representative models of this cutting-edge display equipment are Google Glass, HUD, Flexible Display, and Transparent Display. Google Glass is a kind of wearable computer equipped with Head Mounted Display (HMD), which is under development as an R&D project titled “Project Glass” and will make it easy to realize a ubiquitous digital environment (Furlan, 2013). Google Glass can use many other Google applications including Google Now, Google Map, Google Plus,
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and Gmail. Google Glass can presently display functions including showing information in a hands-free form, interaction through voice command in natural language, video recording, picture taking, video calls, image search, translation, directional guidance, message sending, weather search, and providing flight information. These functions are only the beginning and can be extended, strengthened, and enhanced as much as is desired. The ALA, in its “2014 ALA Midwinter Meeting (http://exhibitors.ala. org/)”, displayed Google Glass and discussed its applicability to the library (ALA, 2013a, 2013b, 2013c). Once a user wearing Google Glass enters a library, the library may display only books of interest to the user on the library wall. The user will not have to look around at all the books in the library, and, if he or she says the title of a book, summary information of that book will be provided. If desired, the library can automatically lend those books in both online and offline formats. The library can also provide immediate translations for search services and metadata through the Glass format. The guide to the library will be taken charge of by Google Glass, even to the physical space, and it will be a great advance in services for the disabled because a sound guide, book reading, and reference services will all be available. Despite this possible vision of the future, researchers and librarians still have plenty of work to do before it can become a reality. For instance, library applications and content must be developed specifically for use with devices like Google Glass. Meanwhile, display environments providing information are rapidly changing and the types of information provided by libraries vary (Ohe, Kume, Demachi, Taguchi, & Ichimura, 1999). Information suitable to the needs of users must be provided through the device and in the form desired by the users. Displays presently available or at a stage of commercialization to be applicable to the library in the near future are HUD, Flexible Display, and Transparent Display (Fig. 4). HUD (Head-Up Display) is a device which allows the pilot of an airplane to accurately view information from instruments and CRT within his or her view, designed to display operating information on the windshield glass of an automobile or airplane (Newman, 1995). At present, it is used for reducing automobile accidents. Flexible Display is a paperlike display known for realizing the same picture quality even if it is
folded or bent (Kirschner & Muller, 1987). This technology will replace existing screens on laptops, PC monitors, and televisions, and is expected to be embraced by the electronics market thanks to the reduction in screen size and volume it represents. Transparent Display is a collective name for a display that is completely see-through when turned off, and remains partially transparent when turned on. This technology, which combines augmented reality and touch screens, has many everyday applications, including living room windows or indoor and outdoor advertisements and PSAs. The future library will construct display environments using the technology described above. INFINITE CREATIVE SPACE Combining infinite creative space with library services is an innovative idea which will have a positive impact on the lives of library users. Infinite creative space in libraries will allow users to see the world differently and give them an opportunity to explore or imagine new possibilities for a future they will create. The concept of infinite creative space is meant to facilitate the creation of something using technology, but does not include only STEM activities. The space is intended to draw creative people, and the infinite creative space movement centered in libraries helps to teach users to think creatively and explore solutions. It is a space where people gather and create new things with certain technologies. The internet age has made users aware of the many different ways to acquire knowledge besides physical books, and therefore librarians have reached for new identities within their core mission of information community helpers. Infinite creative space (or makerspace) is a natural extension of that identity. Camoprodon, Bigazzi, Pineda, Tham, and Mattia (2013) expressed the key concepts surrounding the “Coworking and Makerspaces” movement with “community” as the most important, followed by open, sharing, collaboration, startup, network, makers, and entrepreneurship. Noh (2014) comprehensively examined domestic and foreign examples of infinite creative space construction with previous research and drew up 12 total concepts for the roles of the space: 1) social communication, 2) learning, 3) sharing creative resources, 4) exploring interested topics, 5) job search and assistance for business startups,
HUD Head-UP Display
Flexible Display
Transparent Display
Fig. 4. Changes of display environment.
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Fig. 5. Development process of Library 4.0.
6) finding and cultivating authors, 7) self-publication, 8) idea incubation, 9) cooperative creation, 10) experiencing and utilizing new equipment, 11) storytelling, and 12) expert mentoring and consulting. Because the space has so many varied uses, many potential programs and events covering a wide range of topics could be held there. KEY CONCEPTS OF LIBRARY 4.0 In conclusion, based on the analysis conducted by this research team on the research on each development stage of the web including Web 4.0, NGDLs, other various papers regarding Library 3.0 and 4.0, and the result of reviewing technologies applicable to the library, the generation development process of Library 4.0 is shown in Fig. 5. As shown in the following picture, the era of Library 4.0 is likely to be realized beginning in 2015, and the essential keywords and concepts of Library 4.0 will be Intelligent Library, Makerspace, Context-Aware Technology, Open Source, Big Data, Cloud Service, Augmented Reality, and State-of-the-art Display. Moreover, the role of librarians will be very significant in making all of these concepts applicable to the library; training the Librarian 4.0 must be a top priority. DISCUSSION AND FUTURE STUDIES DISCUSSION In this paper, in order to create a model of Library 4.0 as a NGDLs, the literature and newspaper articles related to information technologies and examples of their application in the library have been collected and analyzed. This chapter concentrates on discussing the research questions raised in the course of conducting that research. First, opinions of scholars tracking the rise of Web 4.0 vary widely, but Web 4.0 features commonly suggested by previous researchers are: reading, writing, and executing simultaneously, intelligencebased agents, connected web, ubiquitous web, intelligence connections, and intelligence-based web. In the terminology cloud regarding Web 4.0, the terms Convergence, Remixability, Standardization, Participation, and Usability are the most prominent. Summarizing the opinions of various scholars, this study defined Web 4.0 as a symbiotic web, a
semantic web (connecting web), a web that reads, writes, and executes simultaneously, a massive web, and an intelligence-based web. Second, the features of Library 4.0 most often suggested by previous research were examined, and it seems that few scholars have tackled this concept in the past. Thus, a broader scope for research collection was defined to forecast a model for a NGDLs and conduct a comprehensive analysis on the concept of Web 4.0. As a result, the features of Library 4.0 were determined as: intelligence-based, massive data, augmented reality, context aware, cutting-edge displays, and infinite creative space. Third, in this context, the keywords that best explain Library 4.0 are: Intelligent, Makerspace, Context-Aware Technology, Open Source, Big Data, Cloud Service, Augmented Reality, State-of-the-art Display, and Librarian 4.0. This study presented the development of Library 4.0 and its keywords for the first time in the LIS field as shown in Fig. 5. Because of this groundbreaking model, this paper has great significance to future research on this topic.
SUGGESTIONS FOR FUTURE STUDIES This paper discusses Library 4.0, although, unfortunately, there were almost no previous studies devoted to the topic. Discussions of Web 4.0, however, are a current topic of research popular among information scientists and internet users alike. Therefore, it is an appropriate time to begin the discussion of Library 4.0 and preparing for future libraries shaped by future technologies. This study aims to suggest a model of Library 4.0 by reviewing the findings of leading researchers who discuss Web 4.0 and next generation digital libraries, since the available information on how Web 4.0 will form Library 4.0 is so far lacking. This study could not provide a very wide range of applications to Library 4.0. It also does not specify recommendations for the application direction. This lack is because there are not enough resources related to the concept and discussion of Library 4.0. Therefore, this study focuses on proposing concepts and keywords. However, research in case studies and specific recommendations for the direction of Library 4.0 must be conducted. Future studies must also concentrate on detailed ways to
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apply Web 4.0 concepts to libraries and what services these libraries will provide to maintain and improve user satisfaction. CONCLUSION Even in 2013, Patel had already begun writing about ideas for Web 5.0. As we enter into the era of Web 4.0 and various new features begin to appear, it is still difficult to clearly define the more distant Web 5.0, but he referred to it as a distributed “Symbionet” web and predicted that we would be able to surf by ourselves in 3D virtual worlds of Symbionet. He said Web 5.0 will connect people through Smart Communicators, expressing them as avatars. Users will interact with content that satisfies their feelings and needs, and avatar facial expressions will change in real time through neural technology. Because library development is so tied to technological developments, it is important to begin a discussion of Library 4.0 as Web 4.0 begins to take shape in reality—and Web 5.0 in the imagination. This paper discusses the development direction of Library 4.0 based on the changes in cutting-edge information technologies and user demands for the library. Keywords for describing Library 4.0 will be Intelligent, Makerspace, Context-Aware Technology, Open Source, Big Data, Cloud Service, Augmented Reality, State-of-the-art Display, and Librarian 4.0. Future studies will need to discuss in more detail how each technology must be applied to the library to best serve users and communities. ACKNOWLEDGMENTS The authors want to thank anonymous reviewers for their thoughtful and constructive comments that helped to improve this paper. The author would also like to thank Patricia Ladd for editing this article into fluent American English. REFERENCES Aghaei, S., Nematbakhsh, M. A., & Farsanim, H. K. (2012). Evolution of the world wide web: From web 1.0 to web 4.0. International Journal of Web & Semantic Technology. 3(1), 1–10. ALA (2013a). 2014 ALA Midwinter Meeting. Retrieved from http://alamw14.ala.org ALA (2013b). Cutting-edge technology in library services case studies. Retrieved from http://www.ala.org/offices/sites/ala.org.offices/files/content/oitp/cuttingedge/2013_ cutting_edge.pdf ALA (2013c, January 22). The ALA honors five local libraries for offering cutting-edge services. Retrieved from http://www.districtdispatch.org/2013/01/cutting-edge-2013/ ALA (2014, February 4). ALA news. Retrieved from http://www.ala.org/news/pressreleases/2014/02/four-local-libraries-honored-offering-cutting-edge-services Alotaibi, S. (2010). Semantic web technologies for digital libraries: From libraries to Social Semantic Digital Libraries (SSDL). Over Semantic Digital Libraries (SDL). The 4th Saudi International Conference. UK: University of Manchester. Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., & MacIntyre, B. (2001). Recent advances in augmented reality. Computer Graphics and Applications, IEEE, 21(6), 34–47. Belling, A., Rhodes, A., Smith, J., Thomson, S., & Thorn, B. (2011). Exploring Library 3.0 and beyond. Available at: http://www.libraries.vic.gov.au/downloads/20102011_Shared_ Leadership_Program_Presentation_Day_/exploring_library_3.pdf. Berners-Lee, T. (2006), “Linked data — Design issues”, http://www.w3.org/DesignIssues/ LinkedData.html/. Breeding, M. (2011). The systems librarian. Preparing for the long-term digital future of libraries. Computers in Libraries, 31(1), 24–26. Burrus, D. (2013). Bigthink: From Web 3.0 to Web 4.0. Retrieved from http://bigthink. com/videos/from-web-30-to-web-40 Callari, Ron (2009). “Web 4.0, Trip Down the Rabbit Hole or Brave New World?”, http:// www.zmogo.com/web/web-40trip-down-the-rabbit-hole-or-brave-new-world. Camoprodon, G., Bigazzi, S., Pineda, P., Tham, C., & Mattia, S. (2013). Samples of ongoing experiences in Europe. Barcelona: Coworking Europe Conference. Casey, M. (2007). Service for the next generation library: A Library 2.0 perspective. Retrieved from: www.librarycrunch.com/2005/10/working_towards_a_definition_o.html. Chad, K., & Miller, P. (2005). Do Libraries Matter? White paper, available at: www.talis. com/downloads/white_papers/DoLibrariesMatter.pdf. Chauhan, S. K. (2009). LIBRARY 4.0.Retrieved from http://key2information.blogspot.kr/ 2009/11/library-40.html. Cho, J. (2012). A study on the cloud collection. Journal of Korean Library and Information Science Society, 43(1), 201–219. Choi, S. & Woo, S. G. (2012). Definition, utilization and trends of big data. Korea Information Processing Society Review, 10(3), 10–19. Chow, A., Baity, C., Chappell, P., Rachlin, D., Vinson, C., & Zamarripa, M. (2010). When real and virtual worlds collide: A public library's management of a second life library. ALA
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Skryabina, A. (2010). Moving forward → Web 3.0 → Web 4.0. Retrieved from http:// iamannaskryabina.blogspot.kr/2010/12/moving-forward-web-30-web-40.html Song, I.J., Cho, W.K., & Cho, T.Y. (2008). A study on the spatial characteristics of ubiquitous environments — Focused on the modern public library. Journal of the Architectural Institute of Korea Planning & Design, 24(6), 3–10. Stephens, M. (2005). ALA TechSource - Do Libraries Matter: On Library & Librarian 2.0. Retrieved Nov. 17, 2006, from American Library Association. Web site: http:// www.techsource.ala.org/blog/2005/11/do-libraries-matter-on-library-librarian20.html. Stephens, M. (2007). "Tools from Web 2.0 & Libraries: Best Practices for Social Software". Library Technology Reports, v. 43, n. 5. http://www.accessmylibrary.com/coms2/ summary_0286-33252266_ITMN. Stephens, Michael T. (2007). Web 2.0 and libraries, Vol. 43(No. 5) ALA Tech Source.
INTEGRATION OF LIBRARY SERVICES WITH INTERNET OF THINGS TECHNOLOGIES
INTEGRATION OF LIBRARY SERVICES WITH INTERNET OF THINGS TECHNOLOGIES by Kyriakos Stefanidis & Giannis Tsakonas The SELIDA framework is an integration layer of standardized services that takes an Internet-of-Things approach for item traceability in the library setting. The aim of the framework is to provide tracing of RFID tagged physical items among or within various libraries. Using SELIDA we are able to integrate typical library services—such as checking in or out items at different libraries with different Integrated Library Systems— without requiring substantial changes, code-wise, in their structural parts. To do so, we employ the Object Naming Service mechanism that allows us to retrieve and process information from the Electronic Product Code of an item and its associated services through the use of distributed mapping servers. We present two use case scenarios involving the Koha open source ILS and we briefly discuss the potential of this framework in supporting bibliographic Linked Data.
Introduction Libraries are trying to identify potential applications for Internet-of-Things (IoT) technologies and a recent survey by OCLC (2015) highlighted that the anticipated uses of IoT are mostly related to intelligent uses of space and facilities. The same survey revealed that the most familiar IoT services to librarians were those designed for inventory purposes. Such services would require the employment of Radio Frequency Identification (RFID) tags as aids for increased visibility and unique identification. RFID have been envisaged as the appropriate technologies to fulfill the promise of IoT in libraries. Fortune (2012) describes a future state where books are tagged with RFID and “sensors are placed on the shelves to detect the removal of any item for consultation” making the shelves “active”. In doing so, libraries must be equipped with the right technology infrastructure, such as sensors, readers, services and software. One of the main technical challenges is that, apart from the lack of appropriate technologies to coordinate these components, any intervention has to be easily adjusted to key software infrastructure, such as the various Integrated Library Systems (ILS). The existing technologies in libraries cannot be ignored or skipped, but on they should be able to easily link to these solutions. In our case this is achieved with the SELIDA framework, on which the main part of this article is focused. The SELIDA framework has been developed in the frame of the self-titled project. Its name stands for the Greek equivalent for the acronym “Printed Material Management Using Radio Frequency Identification Technology” (“selida” in Greek means “page”). SELIDA is a public-private sector collaboration project, with the Library and Information Center at the University of Patras as host of the pilot implementation. The ILS used in this prototype is the open source Koha. A second section of the paper is dedicated to the exploration of the Electronic Product Code (EPC) tags’ potential to store information about library holdings. As the current Linked Data efforts in the bibliographic ecology have not substantially covered the issue of holdings information, we can exploit technologies from other domains. One of the main principles in the Linked Data domain is to reuse already operating or standardized components to avoid ambiguity (Bizer & Heath, 2011) and to this end EPCs can be part of the linking of bibliographic records with holdings information.
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Related technologies The key component of the SELIDA framework is the use of RFID tags as an aid for identification of physical items, and EPC as the underlying building block for standardized tracking services. RFID tags are now commonplace in Libraries, with many advantages over other identification technologies, such as barcodes or QR-codes—namely in bulk detection, reading distance, alignment, capacity and so on. RFID tags are also preferred for supporting real time traceability and for being intrinsically connected to EPCs. According to a recent survey of the GS1 US—the US chapter of GS Global, an organization that promotes the adoption of its standards in industry—retail market stakeholders such as manufacturers and sellers are rapidly adopting RFID with EPC to increase the visibility of their products (GS1 US, 2015). Thus, the use of EPC Global, the GS1 suite of standards and specifications, enables the visibility and traceability of physical units. The EPC standard is mostly used in the supply chain sector and in our case it is adjusted to the physical items of a library’s collections, such as books, discs, etc. The use of EPCs together with the Object Naming Services (ONS) enables the circulation of uniquely tagged information; the items are notated by the tagging services of EPC Global and the information is exchanged with ONS. An EPC can be written on a physical RFID tag and the number of encoded digits is determined by its type (in our case 24 decimal digits). It encodes a Serialized Global Trade Item Number (SGTIN), which is a Global Trade Item Number (GTIN) that is expected to be the same for a set of items, plus a serialization number for each physical unit. The EPC is split into different, fixed-length sections, with each section providing different information. The first section is the header that reports on the coding scheme; the second is the company/ institution prefix, which in our case represents a library; the third section defines the item reference; and the final section refers to its unique serial number. When an RFID reader captures an EPC in binary format, it transmits it to a middleware layer and transforms it in a URI format. Figure 1 presents the sections of a typical EPC as a URI.
! Figure 1. An example of an EPC in URI format Lastly, the Object Naming Services are provided by a resolver, which translates a URI address to a domain name. By working in a way similar to DNS, the ONS resolver splits the binary format to different sections, each of which corresponds to a different server that holds the related information and services about its own section and a pointer to the next server in the URI sequence. Architecture We deploy a three-layer architecture, outlined from bottom to top: 1. The bottom layer is the integration layer that manages seamlessly all the processes relevant to the library services workflows. By seamless we mean that the actual services (i.e. the source code) of the ILS are not changed and the production system remains intact. Therefore any change, such as a version update, in either part does not affect the other. The integration layer is injected upon page load as a JavaScript file. In our
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prototype, we use the mod_substitute directive on the ILS’s dedicated Apache web server. Each time a module/page of interest is requested by the server (i.e., checking out an item), the web server adds a script tag that loads the additional functionality. This layer, in the form of a JavaScript application module (henceforth the SELIDA module), adds the required user interface elements and handles all the necessary web service requests. 2. The middleware layer receives, analyses, processes and propagates the data collected by the RFID readers to the ILS. The middleware hides the complexity of the actual RFID infrastructure and provides only the information about the workflow events. The middleware is agnostic of how the data is handled afterwards. 3. The upper layer, called the services layer, provides secure access to the ONS infrastructure which consists of a mapping server and an ONS resolver. The mapping server manages all provenance data which are relevant to a single physical item, as well as the mapping between an EPC and its metadata. These data can contain information regarding item transactions within locations controlled by the owning library or any other interconnected library, as long as all those are part of the SELIDA infrastructure. Furthermore, through the services layer, the system accesses the ONS Resolver, which allocates specific services to specific types of users, such as inventory services for collection development librarians or history services for circulation librarians. This is accomplished by a web service layer that functions on top of the ONS resolver, which provides authenticated users with the capability to query the whole ONS infrastructure and discover the management services for any given EPC and point them to the correct mapping server.
! Figure 2. The layers of the SELIDA framework architecture
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Implementation In our prototype version, the framework supports two types of services: first is a subset of basic library services, namely checking-out, checking-in, adding, and deleting an item. Secondly, the framework supports some item-related services, namely retrieving information about the history of an item (location of check-ins and check-outs across the interconnected libraries), as well as searching for the physical location of an item within a library. We describe one implementation case for each type of service. Checking-out When a circulation librarian navigates to the check-out module of the ILS by requesting the respective URL, the SELIDA module starts executing upon page load and adds the button “Scan” next to the button “Check-Out” of the Koha interface. When the user presses the button “Scan”, a web service request is launched from the SELIDA module, which starts up the RFID reader via the middleware services. The results, in the form of the items’ titles and codes that the reader captured, are sent back to the SELIDA module. The SELIDA module pops up a window that informs the user of these results. After this presentation, the regular check-out workflow resumes by sending the required POST requests to the Koha web server. Since Koha does not support (at the moment) multiple checkouts, the required multiple requests are sent via AJAX and the results of these requests (namely the errors) are gathered by the SELIDA module for subsequent presentation to the user. When the check-out process ends and the web server responds with the next web page, the SELIDA module sends a second web service request to the middleware indicating that the check-out is complete. History services History services are expected to be used among interconnected libraries. Information about items should adhere to the same API and should be easily stored by the networking parties for reviewing the required data. Therefore a lightweight data exchange mechanism is required and as such the SELIDA module services have been designed following the RESTful architectural style using JavaScript Object Notation (JSON). In our framework, historical data can include a list of transactions only based on the dates of interaction with the middleware, together with a geospatial representation of the location of the respective transaction, i.e. the library where this transaction takes place. Any request for item status, like checking in items, can contain multiple EPC tags at a time, as well as error warnings (0 in case of no error) and relevant metadata for the identified books, such as notes on the purpose of transfer, the owning and/or the target library, etc. The following code snippets in JSON exhibit the request and response for history services. Request 1 2 3 4 5 6
{ "EPCList":[ {"EPC": "961012345678910012345678"}, {"EPC": "961543210109876543210101"} ] }
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Response 1 { 2 "ErrID":0, 3 "EPCList":[ 4 {"EPC":"961012345678910012345678", 5 "Barcode":"910012345678", 6 "Title":"Macroeconomics", 7 "CallNo":"339 PET", 8 "Status":"Available", 9 "Owner":"GR-PaULi", 10 "Location":{ 11 "Lat":"38.28923", 12 "Lon":"21.785369" 13 } 14 }, 15 {"EPC":"961543210109876543210101", 16 "Barcode":"876543210101", 17 "Title":"Macroeconomics", 18 "CallNo":"339 PET", 19 "Status":"Loaned", 20 "Owner":"GR-AtPPV", 21 "Location":{ 22 "Lat":"37.959884", 23 "Lon":"23.719248" 24 } 25 } 26 ] 27 }
Table 1. Request/Response Example Can Linked Data and IoT be Real in the Library? The framework could be a promising start for the extension of linked data models of the bibliographic ecosystem to enable the accommodation of information about holdings. Whereas the Linked Data solutions are rapidly progressing in the library sector, thus creating a conceptual network of knowledge resources, IoT proposals are relatively limited and as previously stated have reached a dead end in linking concepts to holdings. Fast evolving linked data frameworks, such as BibFrame, are currently missing mechanisms for global and unique visibility, identification and traceability of items. For instance, BibFrame stores information about the holdings of an instance in annotation fields, but uses ambiguous and contextdepended information, such as the instance’s call number, which might differ from one library to another. The following JSON example shows how minimal, but concise EPC tagged information can be embedded in an annotation field of a BibFrame record: 1 2 3 4 5 6 7
{ "type": "HeldItem", "id": "0123456789012helditem1", "bf-id": "0123456789012helditem1", "uri": "urn:epc:id:sgtin:96.1.012345.678.910012345678", "urn:epc:id:sgtin:96.1.543210.109.876543210101" "bf-holdingFor": "9600215006instance1" }
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By parsing the EPC of an item in URI format, one could identify a specific physical unit from the collections of a library and retrieve information about its status and location. Thus, there can be an effective and dynamic connection between different “things”—such as works and entities, which have demonstrated that they can be in URI format—and actual things, such as physical items. Discussion The SELIDA framework demonstrates the potential of using already globally working standards and protocols with existing technologies to increase the visibility of physical items with minimal cost and disruption. It also underlines that there is a lot to be done in the future. The penetration of RFID technologies is still slow, especially in academic libraries, where the large number of volumes requires significant investment, while the EPC encoded tags are relatively unknown. Furthermore, there are other kinds of investments and efforts, notably in the area of GS1 company prefixes, which would require the cooperation of authorities to assign global prefixes to libraries. Finally, with the lack of wide application of the STGIN in libraries, there have to be firm decisions about the form of the sections of the 24 digit code. For instance, if a library desires to migrate from an existing item bar-coding scheme, then the number of available item reference digits is limited and will have to refer to another item relation, such as a category (e.g., books or discs). However, if a more global scheme is required, then the item reference will have to be occupied by a worldwide common scheme, like an edition’s barcode, and the rest of the digits used to identify the serialized number of copies of this edition in the library’s collection. In this paper we described the architecture of the SELIDA framework and presented two typical implementation cases. We also raised the issue of using parts of the framework to merge Linked Data with the IoT efforts in the library in a more effective fashion. The services of the SELIDA framework alone can be appealing to networks of libraries, such as cooperating libraries or branch structures that circulate items among them. It is encouraging that IoT technologies are maturing and the potential they hold in identifying and tracing physical objects allow more confident visions for a global library catalog. Appendix A: SELIDA Exhibition Video Video available at https://vimeo.com/lisupatras/theselidaframework and https:// archive.org/details/selida-presentation_201509 Acknowledgements The SELIDA project (#09SYN-72-646) was financially supported by the General Secretariat for Research and Technology (GSRT) of the Hellenic Ministry of Development in the frame of “Cooperation” 2009 Call. About the Authors Kyriakos Stefanidis holds a PhD in Computer Science and a BsC in Computer Engineering from University of Patras (Greece). He is a contracted researcher in University of Patras and ATHENA RC / Industrial Systems Institute. Since 2001 he is involved in a number of research and development projects both European and National. His research interests are code4lib, issue 30
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mainly focused on the field of computer and network security, cyber-physical security and high performance computing. Giannis Tsakonas is Acting Library Director in Library & Information Centre, University of Patras. He holds a BA in Librarianship from the Department of Archives and Library Sciences, Ionian University, Greece and a PhD in Information Science from the same Department. More info at his website www.gtsak.info and/or at his Twitter feed @gtsakonas. References Bizer, C. and Heath T. 2011. Linked Data: Evolving the Web into a Global Data Space. Synthesis Lectures on the Semantic Web: Theory and Technology. New York, NY: Morgan & Claypool Publishers. Fortune M. 2012. Can RFID save Libraries? RFID Arena [Internet]. [Cited 2015 August 11]. Available: http://www.rfidarena.com/2012/11/8/can-rfid-save-libraries.aspx GS1 US. 2015. GS1 US Survey Shows Manufacturers and Retailers Embrace RFID to Enhance Inventory Visibility. [Cited 2015 August 11] Available: http://www.gs1us.org/ about-gs1-us/media-center/press-releases/rfid-survey-findings. OCLC 2015. Libraries and the Internet of Things. NextSpace [Internet]. [Cited 2015 August 11]; 24. Available: http://www.oclc.org/en-europe/publications/nextspace/articles/ issue24/librariesandtheinternetofthings.html.
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INTERNET OF THINGS – POTENTIAL FOR LIBRARIES
Library Hi Tech Internet of Things – potential for libraries Magdalena Wójcik,
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Internet of Things – potential for libraries
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Institute of Library and Information Science, Jagiellonian University, Kraków, Poland
Magdalena Wójcik
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Received 15 October 2015 Revised 14 January 2016 15 February 2016 Accepted 4 March 2016
Library Hi Tech Vol. 34 No. 2, 2016 pp. 404-420 © Emerald Group Publishing Limited 0737-8831 DOI 10.1108/LHT-10-2015-0100
Abstract Purpose – The purpose of this paper is to subject the potential of Internet of Things (IoT) technology for libraries in terms of the possible scope and usage forms of this technology in public and academic library services. Design/methodology/approach – Based on analysis of the subject literature, the main areas of IoT applications in commercial institutions were identified, then an analysis of Library and Information Science English-language literature from the years 2010 to 2015 was conducted in order to create a profile of modern library services. The range of activities of commercial and non-commercial institutions were compared to ascertain if areas in which commercial entities using or planning to use IoT could also be an inspiration for libraries. In this way, a theoretical model of IoT use in library activities was developed. Findings – The research showed that IoT technology might have the potential to be used in library services and other activities, similar to how it is implemented in the commercial sector. Research limitations/implications – The aim of the paper is to determine the possible, not the actual, scope and forms of using this technology in public and academic libraries’ services. Practical implications – The results can be widely used in libraries as an inspiration for the use of IoT technology in modern library services. Social implications – The use of new technologies in libraries can help to improve the image of these institutions in the eyes of users, especially the younger generation. Originality/value – The use of IoT in libraries is a new issue that has not been studied much yet. The issue of using the potential of this technology for the needs of libraries has, in recent years, been raised at international conferences, become a subject of interest to librarian associations, and been widely discussed in the blogosphere, thus proving that this topic is important to practitioners. It is difficult, however, to find any scientific, comprehensive studies of this topic. Keywords Academic libraries, Library services, Public libraries, Internet of Things, Information technologies, Services Paper type Viewpoint
Introduction The development of information and communication technologies is currently rapid, and the consequences of this phenomenon have an impact on libraries (Pellen and Miller, 2012; Aharony, 2014; Boateng and Liu, 2014). After a period of intensive computerization of libraries (Kilgour, 2013), the beginning of the twenty-first century has been a time of great interest in social media issues (Anttiroiko and Savolainen, 2011; Buigues-García and Giménez-Chornet, 2012; Charnigo and Barnett-Ellis, 2013) and mobile technologies (Nowlan, 2013; Ong et al., 2014), but new challenges are constantly being faced by libraries. New technologies such as augmented reality, wearable computing and 3D printing are slowly becoming relevant to library services, forcing continuous development and the need to tailor libraries’ offerings to changing conditions and evolving customer habits (Prince, 2014; Wójcik, 2015). One of the most The author would like to thank Mike Timberlake for proof-reading.
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interesting concepts of recent years, which could potentially be a big challenge for libraries, is the Internet of Things (IoT). The issue of using the potential of this technology for the needs of libraries has, in recent years, been raised at international conferences (Obodovski, 2014), become a subject of interest to librarian associations (ALA, 2015; OCLC, 2015), and been widely discussed in the blogosphere (Mylee, 2011; Potter, 2014), thus proving that this topic is important to practitioners. It is difficult, however, to find any scientific, comprehensive studies of this topic, which is why author decided to address this issue in the hope that this paper will contribute to wider discussion. The subject of this paper is the potential of IoT technology for libraries. The aim is to determine the possible scope and forms of the use this technology in public and academic library services. The specific objectives include: •
define the form of modern library services;
•
determine the potential of the IoT for the improvement of library services;
•
describe examples of IoT applications in libraries; and
•
formulate prospects for the use of the IoT in libraries.
Description of IoT IoT is a concept that has been described extensively since the late 1990s. Many definitions emphasize the different aspects of this issue. According to L. Atzori, A. Iera and G. Morabito it can be described as a “novel paradigm that is rapidly gaining ground in the scenario of modern wireless telecommunications” (Atzori et al., 2010). These authors explained that “the basic idea of this concept is the pervasive presence around us of a variety of things or objects such as Radio-Frequency Identification (RFID) tags, sensors, actuators, mobile phones, etc. which, through unique addressing schemes, are able to interact with each other and cooperate with their neighbors to reach common goals” (Atzori et al., 2010). According to another similar definition: “IoT refers to the networked interconnection of everyday objects, which are often equipped with ubiquitous intelligence” (Xia et al., 2012). The IoT is also considered to be a part of the so-called Future Internet, defined as a “dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols where physical and virtual ‘things’ have identities, physical attributes, virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network” (Vermesan et al., 2011). In summary, it appears that the main idea of the IoT is based on the assumption that everyday objects equipped with appropriate sensors and network access can communicate to fulfill certain tasks. This bold statement raises as much hope as controversy. Enthusiasts of this technology point out the revolutionary nature of the concept and the possibility of its use in almost all areas of life (Guerra, 2012; Suraki and Jahanshahi, 2013). Opponents draw attention to the risks associated with the loss of data privacy, legal problems, and finally the risk of devolving too much control to the devices (Yang et al., 2011; Ziegeldorf et al., 2014). Most researchers and practitioners, regardless of their professed views, are however agreed that IoT is the technology of the future; therefore, the pros and cons of it must be thoroughly recognized, as it will become more and ubiquitous. Literature review To establish the state of research, scientific resources were sourced using Google Scholar. For this author, it was important to comprehensively search for papers that
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represent different authors, publishers and research perspectives. Only articles, books and research reports published between 2010 and 2015 in English were sought. Papers from the same chronological range that were included in the key librarianship databases such as LISTA were also complementarily taken into account, as well as publications, mainly reports, provided by key technology companies from the IoT field, such as CISCO, IBM and Bosch. Based on both full papers and abstracts, the main directions of IoT research were established. These were found to include: general elaboration about the idea of IoT (Weber and Weber, 2010; Kopetz, 2011, Miorandi et al., 2012, Gubbi et al., 2013); forecasts for the development of IoT (Sarma and Girão, 2009; Zorzi et al., 2010; Tan and Wang, 2013); empirical surveys on the use IoT (Atzori et al., 2010; Gluhak et al., 2011; Sheng et al., 2013; Perera et al., 2014); and descriptions of IoT usage in various areas of applications (Tianbo, 2012; Jara et al., 2014; Pang et al., 2014). In general, the topic of IoT use in various scientific disciplines and branches of practical activity is popular. However, there are insufficient papers that approach this issue from the perspective of Library and Information Science (LIS). The following works seem most relevant in the context of this paper: “Ambient findability: libraries, serials, and the internet of things” (Morville and Sullenger, 2010); “Construction of the personalized service system of university libraries in the environment of the internet of things” (Hongbing, 2011); “Smart library and the construction of its service model” (En, 2012); “The new directions of expanding service in colleges and universities libraries under internet of things environment” (Li and Lin, 2013); “An analysis of the conditions for construction of smart library” (Zhuanqin, 2013); “On the construction of Wisdom Libraries in university library” (Fang, 2014); “A framework for citizen participation in the internet of things” (Moreno et al., 2014); “The ‘Internet of Things’: what it is and what it means for libraries” (Hoy, 2015); “Internet of Things and libraries” (Pujar and Satyanarayana, 2015). Unfortunately, many papers related to IoT use in libraries were published in conference proceedings and local journals, thus limiting their accessibility. There are few internationally published articles in major journals in the field of information and library science that are available to the public. Methodology The conducted analysis consisted of several stages (Figure 1). In the first stage, the main types of IoT applications developed by commercial companies were identified, based on analysis of the subject literature and reports provided by key companies in the field. As a result, a little over 50 of the most relevant abstracts or full texts of English-language papers published in 2010-2015 were analyzed. The collected material was subjected to categorization. In the second stage of the study, an analysis of LIS English-language literature from 2010 to 2015 was conducted in order to create a profile of modern library services. A little over 50 articles relating most to the characteristics and forms of modern library services were analyzed. Subsequently, the ranges of activities of commercial and non-commercial institutions were compared. The aim was to ascertain if areas in which commercial institutions using or planning to use IoT might also be inspiration for libraries. Thus, a theoretical model of IoT usage in library activities was developed. In the last stage of the research, both subject literature and network resources were searched to find examples of actual attempts to implement the IoT in libraries. Finally, conclusions about the prospects for the future use of IoT in libraries were formulated. The developed model relates to the potential use of IoT in library services. The author realizes that such considerations always carry a risk of speculation, but believes that a starting point for discussion is needed as this could lead to more research in this area by this and other authors.
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Identification of the main directions of IoT • based on the analysis applications by of the subject literature commercial institutions
Creation of modern libraries’ services profile
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• based on the analysis of the subject literature
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Comparison of commercial and non- • based on theoretical assumptions commercial institutions activities
Finding examples of using IoT in libraries
• based on searching web resources and analysis of subject literature
Formulation of • based on conclusions about the theoretical assumptions and prospects for the analysis of future use of IoT in subject literature libraries
IoT in commercial services In many areas, parallels can be found between the activities carried out by commercial and non-commercial institutions. The LIS area is a good example of this statement. The main mission of libraries – to mediate in communication – is implemented by a number of commercial entities from various industries, often in very interesting and innovative ways. Commercial institutions, defined here as entities whose primary aim is the pursuit of profit by satisfying consumers’ needs, often have financial and technical resources that enable them to deploy new solutions faster than non-commercial institutions. Therefore, it is worth looking into the ideas and experience of commercial entities that could be beneficial to non-profit institutions when conditions are conducive. The analysis of subject literature dedicated to the implementation of the IoT in business, primarily in industries such as communications, management, consulting transport and logistics, make it possible to distinguish several key application areas for this technology. Of course, the diversity of IoT usage ideas is large, but the goal was not to describe it in detail, only to extract some general trends. The summary of this analysis is shown in Figure 2. The analysis showed that commercial institutions use the IoT to share contextual information about products and services. An example might be the trend to send so-called push notifications that display contextual information about products and services on mobile devices, e.g. when passing or staying in a particular place in a mall. This is still more a concept than a widely applicable solution, however, push notifications in their various guises are already part of the shopping experience of many smartphone users with appropriate applications (Pan et al., 2015; Waracle, 2015). Similar solutions are also used in marketing as a part of a participative concept.
Figure 1. Research methodology
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sharing contextual information about companies and products
development of new business models
marketing and promotion
workflow organization
Figure 2. The main areas of IoT use in the commercial sector
track and trace services
logistics, optimization of processes
intelligent buildings, smart cities
The idea is that customers – now called prosumers – can take to the next level the possibilities offered by social media and actively participate in marketing processes by rating and commenting on products or services on an ongoing basis. IoT technology can give them more tools and opportunities to be involved in sharing information and promoting favorite companies and products ( Jara et al., 2014). The IoT is also often used in various contexts related to improved organization, management, and planning. Proof of this is the use of IoT technology in track and trace services, for example in airports, delivery companies and transportation. The possibilities offered by the communication between IoT objects make easier management of standard processes possible in many industries (Michahelles and Cvijikj, 2012; Lopez Research, 2013; CISCO, 2015a). On a larger scale, IoT solutions are used in so-called smart buildings and smart cities (CISCO, 2015b). The idea of smart cities is based on the assumption that the devices can personalize a user’s environment based on information sent by mobile devices (GhaffarianHoseini et al., 2013), offering many still difficult to predict opportunities. Nowadays, many processes implemented in buildings are already automated. Management of air conditioning, temperature or alarm systems is not a problem for modern architecture, but many IoT commentators predict not just smart, but truly intelligent buildings that have the characteristics of artificial intelligence and the ability to independently and accurately make decisions, but this is still in the future (Torres, 2015).
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The IoT is used in logistics and process and workflow organization in various sectors. Using this technology can improve organization, both on the individual and the enterprise level (Giner et al., 2010; Schmidt and Schief, 2010). The IoT allows real-time monitoring and optimizing of processes (Atzori et al., 2010), whether they are related to production, services, trade, storage, transportation, or other business activities. In the subject literature, the IoT is also widely reported to create new business models (Bucherer and Uckelmann, 2011; Glova et al., 2014; Fleisch et al., 2014; Bosch, 2015). It is believed that the IoT can provide the technical and conceptual conditions for the development of new ideas for delivering products and services to clients. According to some authors (Perera et al., 2014), using IoT technology fits well in the global trend of providing all goods as services. The full scope of the use of the IoT for the creation of business models is not yet known, but existing models of potential IoT applications in business are promising (Sun et al., 2012). Modern libraries’ services Moving on to discuss the specifics of modern library services, it can be concluded that despite the passage of time and technological changes, the core of library services is still essentially the mediation of access to information (Wojciechowski, 2014). Providing access to both traditional and online collections is still given in subject literature as the main area of library services. No less important is the area of advisory services and consulting. According to the Pew Report, borrowing books is a very important service for 80 percent of Americans, and another 80 percent claim that help from reference librarians is very important for their library experience (Zickuhr et al., 2013). It is suggested that librarians’ duties include sharing catalogue information, referring to sources of information, and providing facts, thereby actually being a source of information. In some papers, attention has been drawn to the fact that nowadays the role of librarians – especially in academic environments – is not limited to simple information sharing, but is also based on partnership and assisting users, be they researchers or students, in the whole process of obtaining and using data, starting from the point where the idea, need or concept appears, to the moment of publication (Tsang and Renaud, 2014). This approach is related to the idea of so-called embedded librarianship, which is a concept based on deep participation of librarians in the lives of users and providing services at a place and time convenient for them (Shumaker, 2009). Library services are nowadays offered in various forms and with a variety of tools, such as chat, mail, social media, mobile applications, and others (Canuel and Crichton, 2011; Arif and Mahmood, 2012; Ong et al., 2014), but when it comes to their basic nature, it seems that the core of library services can be split into groups (Figure 3). First, the sharing of information in the form of dates, names, facts, etc. Second, the provision of catalog and bibliographic information which assists users with navigating through library resources and searching for relevant sources of information. Third, the provision of access to traditional and online collections in terms of lending materials and making them available on-site or remotely. As was concluded earlier (Tsang and Renaud, 2014), in a modern library service model, providing factual or directory information and providing access to traditional and electronic resources often merge into one complex process which hopes to find a comprehensive solution for a user’s problem. Another aspect of library service models is the provision of space and equipment, for instance library rooms, computers, scanners, information searching tools, etc. Pew Institute research showed that this aspect of library services – particularly providing free access to computers and the internet – is very important
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Figure 3. The main modern library services
sharing factual information
consulting, training
providing access to traditional and online collections
providing directory/catalog information
providing access to space and equipment
for 77 percent of Americans above 16 years old (Zickuhr et al., 2013). The same research also showed that 35 percent of respondents would very likely make use of new electronic devices in libraries (Zickuhr et al., 2013). Finally, there is complex consulting and training on the use of the library, its contents, and about information retrieval in general. In the modern world, libraries play an important role in educating competences in the field of information and media literacy. Providing education in the field of evaluating the quality of information is one of the most important library services (Katz, 2015). It is worth noting that this list does not include the so-called external activities of libraries, such as cultural, local, and community events, etc. Other library processes, such as gathering, describing and storage of collections, were not included either. The developed scheme that covers the main aspects of library services may seem simple, but when the variety of activities undertaken by the libraries is considered it can be seen to be correct. Therefore, this will serve as a basis for further analysis. The IoT in library services: a theoretical model The actions described in this part of the paper are intended to create a theoretical model for areas of IoT application in library services. This idea is based on the assumption that the IoT is universally applicable and can be widely used regardless of the nature of the business profile of a company or institution. To test this hypothesis, the areas of IoT use in various commercial sectors were analyzed to select the most common fields of use, which were then divided into services and other activities. The results of this analysis were compared with a list of the main services and other activities provided by libraries in search of commonalities in terms of functions performed by commercial enterprises and libraries. The author assumed that if the functions/areas of activity of various industries and libraries are similar and commercial businesses use the IoT, then this is also theoretically possible for libraries. The results of this comparison are shown in Tables I and II. The first comparison concerning services showed that the IoT could potentially be used in libraries for providing access to traditional and online collections and providing factual and directory information, as is the case in the commercial sector. For example, this technology could make it easier for librarians and users to locate physical objects
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in the library and navigate through virtual resources. It could also be used to deliver contextual hints and information about resources connected with current user interests. The IoT could also be potentially useful in other areas of library services, such as consulting and training. In this context, the IoT could be used for downloading up-to-date information about users from their mobile devices, for instance their mood, daily schedule, etc., and personalizing training courses according to this data. The IoT could also be used for signaling the availability of facilities and resources, thus preventing user frustration due to lack of space in the reading room or unavailability of workstations. Comparison of other commercial IoT uses against other types of library activities also revealed interesting possibilities. It seems that although the IoT could potentially be frequently used in marketing and promotion, its usefulness is not limited to this area. It could also be used in process optimization, library workflow organization, and the development of innovative business models that make libraries more interesting for users and other stakeholders. Using innovative IoT-based marketing techniques to promote libraries and organize events could help build the image of the library as a modern institution that follows current trends. This technology could also be used to streamline internal library processes. For example, gathering, describing and analyzing resources, smart building technology, and the proper storage of resources. To sum up, it can be concluded that the main potential areas of IoT use in library services are providing directory information and providing access to traditional and online collections. The IoT also has potential for consulting and training, track and trace services, and the sharing of information (Figure 4). Providing contextual information about companies and products Providing access to traditional and online collections Consulting, training Sharing factual information Providing directory information Providing access to space and equipment Notes: 1 – there is a match; 0 – there is no match
Marketing and promotion Gathering 0 Description 0 Storage 0 Analysis and selection of collection 0 Marketing and promotion 1 Beyond services offer 1 Notes: 1 – match exists; 0 – no match
1 1 1 1 0
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Track and trace services 1 0 0 1 1
Logistics, optimization of processes
Workflow organization
1 1 1
1 1 1
0 0 0
0 0 1
1 1 1
1 1 1
0 1 1
0 0 0
Table I. Comparison of IoT’s fields of use in services of commercial companies with the range of modern libraries’ services
Development of Constructing new business smart models buildings
Table II. Comparison of other fields of IoT use in commercial companies with non-service library activities
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providing access to traditional and online collections
providing directory information
Figure 4. The main areas of potential IoT use in library services
consulting, training
IoT in libraries’ services
sharing factual information
track and trace services
In terms of IoT use in other library activities, the most promising areas seem to be marketing, promotion, storage, and cultural activities and events. IoT can also be used in gathering, description and analysis/selection of collections (Figure 5). Overall, it seems that the IoT can theoretically be successfully used in almost every area of library work, including both services and other activities. The IoT in libraries – examples of implementation There are not many existing descriptions of IoT use in libraries, however literature analysis and a search of network resources revealed some interesting initiatives.
marketing and promotion
storage
description
IoT in other libraries’ activities beyond services offer
Figure 5. The main areas of potential IoT use in other library activities
analysis and selection of collection
gathering
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The first is the BluuBeam application implemented by Orlando Public Library. This application is based on the iBeacon technology that is usually used in augmented reality initiatives. BluuBeam sends location-triggered information to mobile devices that helps users search for resources and expand their interests with contextual hints (Sarmah, 2015). According to the assistant director of the Orange County Library System, this application is like a “virtual tap on the shoulder” (Sarmah, 2015), a little reminder and a hint for users. The distinguishing feature of this product is the simplicity of the concept, which is easy to explain to users, and its high utility for contextual search. The BluuBeam technology used by Orlando Public Library is also used – according to the company’s founder – by approximately 30 other libraries in the USA. Unfortunately, other instances of BluuBeam use in libraries are not well described (Swedberg, 2014). Capira Technologies has a rival idea for IoT use in libraries. The solutions offered by this company allow the integration of mobile applications with existing library systems. The application can be tailored to the individual needs of a library and gives broad possibilities: users can receive notifications about the status of their account, can be informed about library events, search through catalogs, or receive personalized and contextual notifications from librarians that are related to their current interests. According to one of Capira’s co-founders, this application is widely used in over 100 libraries (Swedberg, 2014). Examples are Somerset County Library and Half Hollow Hills Community Library (Swedberg, 2014). Conclusions Despite several successful initiatives, the IoT in libraries is still more a concept than reality. It remains to be seen what will happen in the future. In the subject literature, in the blogosphere, and in the public discourse a belief seems to be prevalent about the rapid and inevitable development of IoT technologies (Gubbi et al., 2013; Xu et al., 2014; Roy, 2015). According to some authors: “The Internet-of-Things may represent the next big leap ahead in the ICT sector” (Miorandi et al., 2012). The potential scope of IoT applications in business, science, entertainment and everyday life is huge. In the literature and in the media, different visions of the development of the IoT and the various applications of this technology can be found, some based on existing projects, others still bordering on science fiction. According to Daniel Obodovski, author of The Silent Intelligence: The Internet of Things book and keynote speaker at an American Library Association (ALA) conference: “the Internet of Things is happening, and it is happening now” (Obodovski, 2014). This suggests that although the predicted IoT boom has yet to come, the first signs of this technology are already clearly visible. It seems logical that in this context, libraries will include IoT in their service range to follow global trends and better meet users’ needs. Based on observation of the commercial sector, this paper presents one vision of the potential of IoT for libraries and attempts to identify areas of library activities in which this technology could perform well. Analysis showed that some commercial activities overlap with library services. Therefore, libraries can theoretically use the IoT in a similar way to commercial institutions. The obstacles to the use of IoT in the library services are, of course, financial and organizational, but the potential is clearly visible. In the area of library services, the greatest potential of IoT technology can be seen in process of providing access to traditional and online collections and providing directory information. IoT technology can also be useful for sharing information, conducting consulting or training, and providing access to spaces and equipment.
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IoT technology may also be useful in other non-service library activities such as gathering, description, storage, analysis and selection of collections, marketing and promotion, and event organization. The presented findings are largely consistent with the assumptions presented by other authors and practitioners. Results of the online survey conducted among librarians in 2014 by the Online Computer Library Center showed, for instance, that from the point of view of practitioners, the IoT is a promising technology that can be used in areas such as inventory control, access and authentication, and monitoring of collection storage (OCLC, 2015). Of course, there are some concerns too, mostly related to data privacy and security issues, but they do not outweigh the potential benefits (OCLC, 2015). A similar opinion is presented in a trend analysis prepared by ALA. According to the ALA’s Center for the Future of Libraries, development of the IoT will be rapid: “Estimates of the size of the Internet of Things range from 25 billion to 50 billion objects connected by 2025” (ALA) and it will definitely be one of rising trends important for the future of libraries. Among concerns related to introducing the IoT to the public on a large scale, the ALA listed problems with a lack of standards for data collection, storage and transmission. Another potentially dangerous factor is the threat of a digital divide developing between users and non-users of this technology (ALA). As Potter predicts on his blog, “The Internet of Things will, hopefully, be a big deal in libraries” (Potter, 2014). Among potential areas of IoT use, the author listed: tracking books; organizing self-guided tours; making exhibits in special collections more interesting; providing options for contactless payments; checking availability of equipment; and providing more detailed information about collections (Potter, 2014). A similar conclusion is reached by Engard, according to whom the possibilities of IoT use in libraries are almost endless and depends mostly on the imagination of librarians (Engard, 2015). Among examples of IoT use in libraries, the author listed: using RIFD technology for self-checkout; using floor pressure pads and iBeacons to track users’ movements and provide them with contextual information; using wristbands as library cards, and many others (Engard, 2015). In summary, it seems that IoT technology can be used in libraries to support both back-office processes and services for users. New technologies such as IoT usually bring some potential challenges alongside the benefits and opportunities they offer (Figure 6). The IoT has the potential to improve library services by providing users with tools that allow easy use of libraries, constant contextual help, and personalization processes. The IoT may also make it easier for librarians to perform their jobs through extensive automation of routine tasks. The IoT may be a good tool for building the positive image of libraries as modern and constantly developing institutions. On the other hand, questions arise about privacy and data security issues, especially the ethical and legal aspects of data collection and processing and the safety and privacy of users’ data. Careful consideration is required of whether libraries have the financial and technical means to ensure data security and are ready to bear the legal and moral consequences of any failure in this regard. There are also some financial and organizational barriers. Implementation of the IoT requires a lot of financial, technological and organizational expenditure, which may be beyond the capabilities of libraries. Librarians should think about the funding of such initiatives and make accurate business analysis of the profitability of the IoT introduction in libraries. However, the consequences of implementing any technology are often difficult to predict, especially because in the
Benefits
Improving services (example: checking availability of equipment and providing more detailed, contextual information about collection)
Streamlining back-office processes
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(example: inventory control, organizing access and authentication processes, monitoring of collection storage)
Education (example: organizing selfguided tours for users)
Promotion and PR the use of IoT as a promotion tool and a chance to build a positive image of libraries as modern institutions
Barriers
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Data privacy concerns and data security issues (example: ethical and legal aspects of data collection and processing, the safety and privacy of users’ data)
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Lack of standards (example: for IoT data collection, storage or transmission)
Digital divide developing digital divide between users and non-users of IoT technology
Financial and organizational barriers implementation of IoT requires financial, technological and organizational expenditures, which may be beyond the capabilities of libraries
case of libraries the issue is not about financial return, but more about benefits to the community. Another barrier may be the lack of IoT data collection, storage and transmission standards. This is new, unknown territory which librarians will have to investigate and work out new solutions for from scratch. It can be assumed that this will not be an easy process. Finally yet importantly is the problem of the digital divide. For many library clients the new technology is attractive and beneficial, but there are also users who may feel excluded and lost, such as the elderly or the less technically competent. The priority for libraries should be to develop solutions that help tame new the technologies and overcome the concerns of users, before offering new solutions. Overall, it seems that introducing the IoT in libraries is an imminent and inevitable prospect that brings both great prospects and challenges. Therefore, it is particularly worth immediately discussing the pros and cons of this issue in order to be prepared for the future and efficiently prevent problems in order to gain the most benefits from the development of this technology.
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Figure 6. IoT in libraries – benefits and barriers (summary)
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Xu, L., He, W. and Li, S. (2014), “Internet of Things in industries: a survey”, IEEE Computer Society, IEEE Transactions on Industrial Informatics, Vol. 10 No. 4, pp. 2233-2243, available at: http://syslog.co.in/files/ecwsn/Internet%20of%20Things%20in%20Industries%20A% 20Survey.pdf (accessed March 16, 2015). Yang, G., Geng, G., Du, J., Liu, Z. and Han, H. (2011), “Security threats and measures for the Internet of Things”, Journal of Tsinghua University Science and Technology, Vol. 51 No. 10, pp. 1335-1340. Zhuanqin, L. (2013), “An analysis of the conditions for construction of smart library”, Research on Library Science, Vol. 14, available at: http://en.cnki.com.cn/Article_en/CJFDTOTALTSSS201314001.htm (accessed March 16, 2015). Zickuhr, K., Rainie, L. and Purcell, K. (2013), “Library services in the digital age”, available at: http://libraries.pewinternet.org/2013/01/22/library-services/ (accessed March 14, 2015). Ziegeldorf, J., Morchon, O. and Wehrle, K. (2014), “Privacy in the Internet of Things: threats and challenges”, Security and Communication Networks, Vol. 7 No. 12, pp. 2728-2742. Zorzi, M., Gluhak, A., Lange, S. and Bassi, A. (2010), “From today’s intranet of things to a future internet of things: a wireless-and mobility-related view”, Wireless Communications, Vol. 17 No. 6, pp. 44-51. Further reading Pellen, R. and Miller, W. (2014), Innovations in Science and Technology Libraries, Taylor and Francis, Hoboken. About the author Magdalena Wójcik (PhD) is a Lecturer at the Jagiellonian University, Kraków (Poland). Magdalena Wójcik is interested in new technologies, particularly Web 2.0, augmented reality, wearable computing and Internet of Things and their impact on libraries’ services. Magdalena Wójcik newest publications are: The Use of Web 2.0 Services by Urban Public Libraries in Poland: Changes over the Years 2011‐2013 and Potential use of Augmented Reality in LIS Education. Magdalena Wójcik can be contacted at: [email protected]
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INTERNET OF THINGS APPLICATIONS, CHALLENGES AND RELATED FUTURE TECHNOLOGIES
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Internet of Things Applications, Challenges and Related Future Technologies Article · January 2017
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Internet of Things Applications, Challenges and Related Future Technologies Zeinab Kamal Aldein Mohammeda, Elmustafa Sayed Ali Ahmedb Electrical and Electronic Engineering Department, Red Sea University, Sudan a,b
E-mail address: [email protected] , [email protected]
ABSTRACT Nowadays Internet of Things (IoT) gained a great attention from researchers, since it becomes an important technology that promises a smart human being life, by allowing a communications between objects, machines and every things together with peoples. IoT represents a system which consists a things in the real world, and sensors attached to or combined to these things, connected to the Internet via wired and wireless network structure. The IoT sensors can use various types of connections such as RFID, Wi-Fi, Bluetooth, and ZigBee, in addition to allowing wide area connectivity using many technologies such as GSM, GPRS, 3G, and LTE. IoT-enabled things will share information about the condition of things and the surrounding environment with people, software systems and other machines. by the technology of the IoT, the world will becomes smart in every aspects, since the IoT will provides a means of smart cities, smart healthcare, smart homes and building, in addition to many important applications such as smart energy, grid, transportation, waste management and monitoring . In this paper we review a concept of many IoT applications and future possibilities for new related technologies in addition to the challenges that facing the implementation of the IoT. Keywords: IoT Applications, Future Technologies, Smart Cities, Smart Environment, Smart Energy and Grid, Smart Manufacturing, Smart Healthcare
World Scientific News 67(2) (2017) 126-148
1. INTRODUCTION The Internet of Things (IoT), sometimes referred to as the Internet of Objects, will change everything including ourselves. The Internet has an impact on education, communication, business, science, government, and humanity [1]. Clearly, the Internet is one of the most important and powerful creations in all of human history and now with the concept of the internet of things, internet becomes more favorable to have a smart life in every aspects [2]. Internet of Things is a new technology of the Internet accessing. By the Internet of Things, objects recognize themselves and obtain intelligence behavior by making or enabling related decisions thinks to the fact that they can communicate information about themselves [3] . These objects can access information that has been aggregated by other things, or they can added to other services [3]. Figure 1 reviews that with the internet of things, anything’s will able to communicate to the internet at any time from any place to provide any services by any network to anyone. this concept will create a new types of applications can involve such as smart vehicle and the smart home, to provide many services such as notifications, security, energy saving, automation, communication, computers and entertainment [4,5].
Figure 1. Internet of things Concept
By developing the IoT technology, testing and deploying products it will be much close to implementing smart environments by 2020 [6]. In the near future, storage and communication services will be highly pervasive and distributed: people, machines, smart objects, surrounding space and platforms connected with wireless/wired sensors, M2M devices, RFID tags will create a highly decentralized resources interconnected by a dynamic network of networks [7]. In the IoT, the communication language will be based on interoperable protocols, operating in heterogeneous environments and platforms [8]. IoT in this context is a generic term and all objects can play an active role to their connection to the Internet by creating smart environments, where the role of the Internet has changed [9].
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The aim of this paper is presents the internet of things Applications, Related Future Technologies, and challenges .The remainder of this paper is structured as follows: section 2 provides a concept of internet of things Standardizations. In section 3 the application of internet of thing will be discussed. Section 4 will provide Internet of Things and Related Future Technologies and the challenges that facing the IoT will be reviewed in section 5. Finally the chapter will ended by a conclusion of the overall sections.
2. INTERNET OF THINGS STANDARDIZATIONS AND PROTOCOLS By the 2020 around 50 to 100 billion things will be connected electronically by internet . Figure 2 shows the growth of the things connected to the internet from 1988 to forecast 2020. The Internet of Things (IoT) will provide a technology to creating the means of smart action for machines to communicate with one another and with many different types of information [11]. The success of IoT depends on standardization, which provides interoperability, compatibility, reliability, and effective operations on a global scale [12]. Today more than 60 companies for leading technology, in communications and energy, working with standards, such as IETF, IEEE and ITU to specify new IP based technologies for the Internet of Things [13]. [10]
Figure 2. Internet of Things Growth
The design of the IoT standards is required to consider the efficient use of energy and network capacity, as well as respecting other constraints such as frequency bands and power levels for radio frequency communications [14,15]. As IoT evolves, it may be necessary to -128-
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review such constraints and investigate ways to ensure sufficient capacity for expansion, for example in case of additional radio spectrum allocation as it becomes available [16]. IEEE Standards Association (IEEE-SA) develops a number of standards that are related to environment need for an IoT. The main focus of the IEEE standardization activities are on the Physical and MAC layers [17]. The IEEE provides an early foundation for the IoT with the IEEE802.15.4 standard for short range low power radios, typically operating in the industrial, scientific and medical band in addition to use ZigBee technology [18]. The IEEE-SA has an over 900 active standards and more than 500 standards under development. In its research into IoT, it has identified over 140 existing standards and projects that are relevant to the IoT. The base project related to IoT is IEEE P2413 which it is currently considering the architecture of IoT [19][20]. ETSI produces globally applicable standards for information and communications technologies (ICT), including fixed, mobile, radio, converged, broadcast and Internet technologies, discusses a similar concept under the label of “machine to machine (M2M) communication. These standards are considered as one of the basic standards of IoT, because its associate with M2M technology which is one of the basic techniques related to IoT [21,22]. Internet Engineering Task Force (IETF) is concerned with the evolution of the Internet architecture and the smooth operation of the Internet and known as large, open to international community of network designers, operators, vendors and researchers [23]. IETF provides its own description of IoT which provides a most recognizable enhancement to support IPv6, with the 6LoWPAN [24-26]. The 6TiSCH Working Group is being formed at the IETF to address the networking piece of that unifying standard. Based on open standards, 6TiSCH will provide a complete suite protocols for distributed and centralized routing operation over the IEEE802.15.4e TSCH MAC [27]. ITU's Telecommunication Standardization Sector (ITUT) considered as a first organization of standards development and coordination of the Internet of Things. They buts standards to gain benefit of integrated information processing capacity, and industrial products with smart capabilities [28,29]. In addition to make development on electronic identities that can be queried remotely, or be equipped with sensors for detecting physical changes around them.
3. INTERNET OF THINGS APPLICATIONS Internet of things promises many applications in human life, making life easier, safe and smart. There are many applications such as smart cities, homes, transportation, energy and smart environment. A. Smart Cities Many major cities were supported by smart projects, like Seoul, New York, Tokyo, Shanghai, Singapore, Amsterdam, and Dubai. Smart cities may still be viewed as a cities of the future and smart life, and by the innovation rate of creating smart cities today’s, it will became very feasible to enter the IoT technology in cities development [30]. Smart cities demand require careful planning in every stage, with support of agreement from governments, citizens to implement the internet of things technology in every aspects. By the IoT, cities can be improved in many levels, by improving infrastructure, enhancing public transportation
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reducing traffic congestion, and keeping citizens safe, healthy and more engaged in the community as shown in Figure 3 [31]. By connection all systems in the cities like transportation system, healthcare system, weather monitoring systems and etc., in addition to support people by the internet in every place to accessing the database of airports, railways, transportation tracking operating under specified protocols, cities will become smarter by means of the internet of things [32,33].
Figure 3. Smart Cities Aspects
B. Smart Home and Buildings Wi-Fi’s technologies in home automation has been used primarily due to the networked nature of deployed electronics where electronic devices such as TVs, mobile devices, etc are usually supported by Wi-Fi [34]. Wi-Fi have started becoming part of the home IP network and due the increasing rate of adoption of mobile computing devices like smart phones, tablets, etc. For example a networking to provide online streaming services or network at homes, may provide a mean to control of the device functionality over the network [35]. At the same time mobile devices ensure that consumers have access to a portable ‘controller’ for the electronics connected to the network. Both types of devices can be used as gateways for IoT applications [36] . Many companies are considering developing platforms that integrate the building automation with entertainment, healthcare monitoring, energy monitoring and wireless sensor monitoring in the home and building environments [37]. By the concept of the internet of things, homes and buildings may operate many devices and objects smartly, of the most interesting application of IoT in smart homes and buildings are smart lighting, smart environmental and media, air control and central heating, energy management and security as shown in Figure 4 below.
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Figure 4. Smart Home & building applications
Wireless sensor networks (WSNs) with integration to the internet of things technology will provides an intelligent energy management in buildings, in addition to the obvious economic and environmental gains. Internet together with energy management systems also offers an opportunity to access a buildings’ energy information and control systems from a laptop or a smartphone placed anywhere in the world [38]. The future Internet of Things, will provide an intelligent building management systems which can be considered as a part of a much larger information system used by facilities managers in buildings to manage energy use and energy procurement and to maintain buildings systems [39,40]. C. Smart Energy and the Smart Grid A smart grid is related to the information and control and developed to have a smart energy management [41]. A smart grid that integrate the information and communications technologies (ICTs) to the electricity network will enable a real time, two way communication between suppliers and consumers, creating more dynamic interaction on energy flow, which will help deliver electricity more efficiently and sustainably [42]. The Key elements of information and communications technologies will include sensing and monitoring technologies for power flows; digital communications infrastructure to transmit data across the grid; smart meters with in home display to inform energy usage; coordination, control and automation systems to aggregate and process various data, and to create a highly interactive, -131-
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Figure 5. Smart grid applications Today’s grid is very reliable and can deal with normal electricity fluctuations and it will take a step further towards using a low carbon energy system, by allowing integration between the renewable energy and green technologies, and offering many benefits to customer in cost savings through efficient energy use at home [44]. D. Smart Health A close attention that required to hospitalized patients whose physiological status should be monitored continuously can be constantly done by using IoT monitoring technologies. For smart health sensors are used to collect comprehensive physiological
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information and uses gateways and the cloud to analyze and store the information and then send the analyzed data wirelessly to caregivers for further analysis and review as shown in Figure 6 below [45]. It replaces the process of having a health professional come by at regular intervals to check the patient’s vital signs, instead providing a continuous automated flow of information. In this way, it simultaneously improves the quality of care through constant attention and lowers the cost of care by reduces the cost of traditional ways of care in addition to data collection and analysis [46].
Figure 6. Smart healthcare concept Many peoples around the worlds are suffering from the bad health because they don’t have ready access to effective health monitoring and may be a suspected to be as critical situation patients. But with small, powerful wireless solutions connected through the IoT are now making possible for monitoring to come to these patients. These solutions can be used to securely capture patient health data from a variety of sensors, apply complex algorithms to analyze the data and then share it through wireless connectivity with medical professionals who can make appropriate health recommendations [47]. E. Smart Transportation and Mobility The development in transportation is one of the factors to indicate the wellbeing of the country. A road condition monitoring and alert application is one of the most important of IoT transformation application [48]. The main idea of the concept of smart transportation and mobility is to apply the principles of crowd sourcing and participatory sensing. The process began with user identified the route wishes and marked some points as pothole in the smart phone's application [49]. The smart transportation is deal with three main conceptions as shown
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in Figure 7, they are transportation analytic, transportation control, and vehicle connectivity. The transportation analytic represents the analysis of demand prediction and anomaly detection. The routing of vehicles and speed control in addition to traffic management are all known as transportation control which they actually tightly related to the way of the vehicles connectivity (V2X communication), and overall governed by multi-technology dissemination.
Figure 7. Smart Transportation Aspects
IoT can also be used in transportation is an electric vehicles, which is an important means to reduce both the fuel cost and the impact of global warming have also gained considerable attention from drivers. Government in many countries has supported researches on systems to monitor performance of Lithium-ion (Li-on) battery for electric vehicle as explored. The system presented was designed to detect the functions of Li-on power battery by deriving the driving situation from the realistic working conditions for driver so that the driver was able to get the idea of the route status. This solution was embedded with many essential functions such as dynamic performance test of the Li-on battery, remote monitoring with on-line debugging and error correction that could significantly reduce the maintenance cost [50]. F. Smart Factory and Smart Manufacturing Smart factory added a new values in manufacturing revolution by integrates artificial intelligence, machine learning, and automation of knowledge work and M2M communication with the manufacturing process [51]. The smart factory will fundamentally change how products are invented, manufactured and shipped. At the same time it will improve worker
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safety and protect the environment by enabling low emissions and low incident manufacturing. These advances in the way machines and other objects communicate and the resulting way in which decision-making moves from humans to technical systems means that manufacturing becomes “smarter” [52]. new technologies such ; Automation, robotics, and autonomous mobility are all provides a means of smart manufacturing but M2M communications enabled by the “industrial” internet of things will provides a full meaning of smart factory and smart manufacturing by the way of Big Data concept which in this context, refers to the analytical possibilities offered by the volume and variety of data that is generated by a networked economy to optimize the industrial processes to implying less maintenance downtime, fewer outages and much reduced energy consumption [53]. Industries and manufacturing revolution became one of the most developed technologies nowadays, the growth of the industry evolution taken many generations. The first generation related to the mechanical machines in addition to water and stream power. The second industry generation deal with mass production, assembly lines and electricity. In the end of the last century, industries operated under control of computers and automation which recognized as third generation of industries. The smart industry as a fourth generation known as industry 4.0 is based on cypher physical systems which can able to connect with the internet. The industry 4.0 concept with the internet of things can achieve a great expectations for industries resolution deals with many aspects a shown in Figure 8. By introducing the high-tech strategy 2020 initiative focusing the country’s research and innovation policy on selected forward-looking projects related to scientific and technological developments.
Figure 8. Smart Factory (Industry 4)
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G. Smart Environment
Figure 9. Smart Environment based internet of things
Environment plays a major effect in human life. People, even animals, birds, fishes and plants may be affected in unhealthy environment. There were many researches efforts has been paid to solve the problems of environmental pollution and waste resources [54]. Creating of a healthy environment is not easy because of industries and transportations wastes, with irresponsible human activities are daily factors that make the environment damaged [55]. The environment needs a smart ways and new technologies for monitoring and management. Monitoring the environment is important in order to assess the current condition of the environment to takes correct life decision according to collected data from monitoring systems, and management is needed to have an efficient resources consuming and use in addition to decrease the factories and vehicles wastes. Both monitoring and waste management provide a large amount of data to force the health standard by governments or
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healthy environment organizations to protect people and environment, and to mitigate or to avoid natural disaster that might occur [56]. Smart environment is an important technology in our everyday life which provides many facilities and solutions for many environmental applications such as water and air pollution, weather and radiation monitoring, waste management, natural disaster, and many other environment indicators as shown in Figure 9 and all may connected to each persons through home area network. Smart environment devices integration with Internet of Things (IoT) technology is developed for tracking, sensing and monitoring objects of environment which provide potential benefits to achieve a green world and sustainable life [57]. There are many applications of internet of things in environment and that can be divided to two main categories environmental resources management, and environmental quality and protection management [58]. The resources management relates to all natural resources include animals, planets and forests, birds and fishes, coal , petroleum , land, freshwater, air and heavy metals including gold, copper and iron. All these resources are likely to decrease significantly or affected by several factors, including pollution, waste, and abuse. IoT can provides an effective way to communicate between each of these resources sensors with research and monitoring centers to make appropriate decisions in the consumption of these sources. Renewable resources include sunlight, and wind also can be managed and sensed to Ideal use in several uses, such as the provision of renewable energy sources. IoT can control these sources and their use in a number of important applications in the environment [59]. The IoT technology is able to monitoring and managing the air quality by to collecting data from remote sensor across the city, and providing full-time geographic coverage to achieve a way of better managing urban traffic in major cities [60]. The IoT also can be used to measure the levels of pollution in water in order to inform decisions on water usage and treatment. Waste management is also one of the most important environment issues [61]. The various types of waste material chemical or elements can pollute the environment and threaten life in a number of ways in ground effect on animals, peoples and plants and in addition to air and water. IoT provides an environmental protection means by control the industrial pollution by real time monitoring and management systems integrated to supervision and decisionmaking networks to reduce waste, and improved environment [62]. Other environment aspect is a weather forecast and monitoring. IoT can provide a high resolution, and accuracy for weather monitoring by data exchange and information sharing. It’s enabling weather systems to collect data from various vehicles on the road, and wirelessly communicate to the weather stations to support data that is inclusive of air temperature, barometric pressure, visibility or light, motion and other data needed. Sensors equipped in many buildings, vehicles integration with IoT help in collecting weather data which is further stored in clouds for analysis [63]. Radiation of course is one of the most serious problems facing the safety of the environment. The radiation produced by nuclear power plants and some industries negatively affected safety of an environmental and human health, animal and agricultural productivity [64] . For nuclear radiations, radiation control IoT sensor network is able to continuous monitoring of radiation levels around nuclear facilities for leakage detection and propagation prevention [65]. The sensors network formed by wireless link dozens of sensor devices in areas surroundings nuclear power plants with closes proximity to cities [66]. A natural disaster is a major adverse event resulting from natural processes of the earth include floods, volcanic eruptions, earthquakes, hurricanes, wildfires, blizzards and, and other
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geologic processes. IoT can avoid or reduce the impact of a large number of natural disasters that affect in many aspects of life through the distribution of a number of sensor systems for many types of natural disasters and linking these systems with research and rescue announcement stations, also for declaration of emergency networking with hospitals and police stations [67]. IoT will provides a means of smart agriculture and adding great potential in resource saving. By using sensors networks, and scientific research databases, growing of plants and other agriculture productions needed by humans like vegetables and fruits can monitored and save their production processes based on managing many resources such as weather, water and sunlight. In addition, the IoT for environmental monitoring can aid in measuring emissions from factories detect forest fires or aid in agriculture [68].
4. INTERNET OF THINGS CHALLENGES The fact that Internet of things applications and scenarios outlined above are very interesting which provides technologies for smart every things. , but there are some challenges to the application of the Internet of Things concept in cost of implementation. The expectation that the technology must be available at low cost with a large number of objects. IoT are also faced with many other challenges [69,70], such as: Scalability: Internet of Things has a big concept than the conventional Internet of computers, because of things are cooperated within an open environment. Basic functionality such as communication and service discovery therefore need to function equally efficiently in both small scale and large scale environments. The IoT requires a new functions and methods in order to gain an efficient operation for scalability. Self-Organizing: Smart things should not be managed as computers that require their users to configure and adapt them to particular situations. Mobile things, which are often only sporadically used, need to establish connections spontaneously, and able to be organize and configure themselves to suit their particular environment. Data volumes: Some application scenarios of the internet of things will involve to infrequent communication, and gathering information’s form sensor networks, or form logistics and large scale networks, will collect a huge volumes of data on central network nodes or servers. The term represent this phenomena is big data which is requires many operational mechanism in addition to new technologies for storing, processing and management. Data interpretation: To support the users of smart things, there is a need to interpret the local context determined by sensors as accurately as possible. For service providers to profit from the disparate data that will be generated, needs to be able to draw some generalizable conclusions from the interpreted sensor data. Interoperability: Each type of smart objects in Internet of Things have different information, processing and communication capabilities. Different smart objects would also be subjected to different conditions such as the energy availability and the communications bandwidth requirements. To facilitate communication and cooperation of these objects, common standards are required. -138-
World Scientific News 67(2) (2017) 126-148 Automatic Discovery: In dynamic environments, suitable services for things must be automatically identified, which requires appropriate semantic means of describing their functionality. Software complexity: A more extensive software infrastructure will be needed on the network and on background servers in order to manage the smart objects and provide services to support them. that because the software systems in smart objects will have to function with minimal resources, as in conventional embedded systems. Security and privacy: In addition to the security and protection aspects of the Internet such in communications confidentiality, the authenticity and trustworthiness of communication partners, and message integrity, other requirements would also be important in an Internet of Things. There is a need to access certain services or prevent from communicating with other things in IoT and also business transactions involving smart objects would need to be protected from competitors’ prying eyes. Fault tolerance: Objects in internet of things is much more dynamic and mobile than the internet computers, and they are in changing rapidly in unexpected ways. Structuring an Internet of Things in a robust and trustworthy manner would require redundancy on several levels and an ability to automatically adapt to changed conditions. Power supply: Things typically move around and are not connected to a power supply, so their smartness needs to be powered from a self-sufficient energy source. Although passive RFID transponders do not need their own energy source, their functionality and communications range are very limited. Hopes are pinned on future low power processors and communications units for embedded systems that can function with significantly less energy. Energy saving is a factor not only in hardware and system architecture, but also in software, for example the implementation of protocol stacks, where every single transmission byte will have to justify its existence. Wireless communications: From an energy point of view, established wireless technologies such as GSM, UMTS, Wi-Fi and Bluetooth are far less suitable; more recent WPAN standards such as ZigBee and others still under development may have a narrower bandwidth, but they do use significantly less power.
5. INTERNET OF THINGS AND RELATED FUTURE TECHNOLOGIES Many new technologies are related to IoT to prove the integration of wired as well as wireless control, communication and IT technologies together which are responsible for connecting several subsystems and things which operate under a unified platform controlled and managed smartly. A. Cloud Computing The two worlds of Cloud and IoT have seen a rapid and independent evolution. These worlds are very different from each other, but their characteristics are often complementary in general, in which IoT can benefit from the virtually unlimited capabilities and resources of -139-
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cloud to compensate its technological constraints for example storage, processing, and communication [71]. Cloud can offer an effective solution for IoT service management and composition as well as for implementing applications and services that exploit the things or the data produced by them .On the other hand, cloud can benefit from IoT by extending its scope to deal with real world things in a more distributed and dynamic manner, and for delivering new services in a large number of real life scenarios. In many cases, Cloud can provide the intermediate layer between the things and the applications, hiding all the complexity and functionalities necessary to implement the latter. This will impact future application development, where information gathering, processing, and transmission will generate new challenges, especially in a multi cloud environment or in fog cloud [72]. Cloud facilitates for IoT application to enabling data collection and data processing, in addition to rapid setup and integration of new things, while maintaining low costs for deployment and for complex data processing [73]. Cloud is the most convenient and cost effective solution to deal with data produced by IoT and, in this respect, it generates new opportunities for data aggregation, integration, and sharing with third parties. Once into Cloud, data can be treated as homogeneous through well-defined APIs, can be protected by applying top level security, and can be directly accessed and visualized from any place [74]. B. Big Data Due to the rapid expansion in the networks nowadays, the number of devices and sensors in networks are increased more and more in the physical environments which will change the information communication networks, services and applications in various domains [75]. The expectations in the next year’s show that around 50 billion devices will generate large volumes of data from many applications and services in a variety of areas such as smart grids, smart homes, healthcare, automotive, transport, logistics and environmental monitoring. The related technologies and solutions that enable integration of real world data and services into the current information networking technologies are often described under the term of the Internet of Things (IoT) [76]. The volume of data on the Internet and the Web is still growing, and everyday around 2.5 quintillion bytes of data is created and it is estimated that 90% of the data today was generated in the past two years. Collected data from sensors related to different events and occurrences can be analyzed and turned into real information to give us better understanding about our physical world and to create more value added products and services. Such these sensory data like data of predicted and balanced power consumption in smart grids, analyzed data of pollution, weather and congestion , sensory data recorded to provide better traffic control and management, and monitoring and processing health signals data that collected by sensory devices to provide better healthcare services [77]. In addition, the information available from social media such as Facebook, tweeter, WhatsApp and user submitted physical world observations and measurements also provide a huge amount of data (Big Data) [78]. Integration of data from various physical, cyber, and social resources with the IoT enables developing applications and services that can incorporate situation and context awareness into the decision making mechanisms and can create smarter applications and enhanced services. With large volumes of distributed and heterogeneous IoT data, issues related to interoperability, automation, and data analytics will require common description and data representation frameworks in addition to machine readable and interpretable data descriptions [79].
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C. Security and Privacy Due the fact that IoT applications able to access the multiple administrative domains and involve to multiple ownership regimes , there is a need for a trust framework to enable the users of the system to have confidence that the information and services being exchanged can indeed be relied upon [80]. The trust framework needs to be able to deal with humans and machines as users, for it needs to convey trust to humans and needs to be robust enough to be used by machines without denial of service. The development of trust frameworks that address this requirement will require advances in areas such as lightweight public key infrastructures (PKI) as a basis for trust management [81]. Lightweight key management systems is used to enable trust encryption materials using minimum communications and processing resources, as is consistent with the resource constrained nature of many IoT devices [82]. IoT based systems require a quality of information for metadata which can be used to provide an assessment of their liability of IoT data. A novel methods is required for IoT based systems for assessing trust in people, devices and data. One of the most methods used are trust negotiation that allows two parties to automatically negotiate, on the basis of a chain of trust policies, the minimum level of trust required to grant access to a service or to a piece of information. Internet of things uses a methods for access control to prevent untrusted data breaches by control the process of ensuring the correct usage of certain information according to a predefined policy after the access to information is granted [83]. Recently, the IoT becomes a key element of the future internet, the need to provide adequate security for the IoT infrastructure becomes ever more important. A large scale applications and services based on the IoT are increasingly vulnerable to disruption from attack or information theft. Many advanced security methods are required in several areas to make the IoT secure from attacks, thefts and many other security problems such as DoS/DDOS attacks, compromised nodes, and malicious code hacking attacks, that because the IoT is susceptible to such attacks and will require specific techniques and mechanisms to ensure that transport, energy, city infrastructures cannot be disabled or subverted [84]. The IoT requires a variety of access control and associated accounting schemes to support the various authorization and usage models that are required by users. The heterogeneity and diversity of the devices/gateways that require access control will require new lightweight schemes to be developed [85]. The IoT needs to handle virtually all modes of operation by itself without relying on human control. New techniques and approaches for example like machine learning, are required to lead to a self-managed IoT. Cryptographic techniques is also very important in IoT based systems for enable a means of protection for data to be stored processed and shared, without the information content being accessible to other parties. Technologies such as homomorphic and searchable encryption are potential candidates for developing such approaches [86]. D. Distributed Computing Distributed computing uses groups of networked computers for the same computational goal. Distributed Computing has several common issues with concurrent and parallel computing, as all these three fall in the scientific computing field. Nowadays, a large amount of distributed computing technologies coupled with hardware virtualization, service oriented
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architecture, and autonomic and utility computing have led to cloud computing. Internet of Things with distributed computing represents a vision in which the Internet extends into the real world embracing everyday objects. Physical items are no longer disconnected from the virtual world, but can be remotely controlled and can act as physical access points to Internet services [87]. E. Fog Computing Fog computing is related to the edge computing in the cloud. In contrast to the cloud, fog platforms have been described as dense computational architectures at the network’s edge. Characteristics of such platforms reportedly include low latency, location awareness and use of wireless access. While edge computing or edge analytics may exclusively refer to performing analytics at devices that are on, or close to, the network’s edge, a fog computing architecture would perform analytics on anything from the network center to the edge. IoT may more likely be supported by fog computing in which computing, storage, control and networking power may exist anywhere along the architecture, either in data centers, the cloud, edge devices such as gateways or routers, edge equipment itself such as a machine, or in sensors [88].
6. CONCLUSIONS Internet of things is a new technology which provides many applications to connect the things to things and human to things through the internet. Each objects in the world can be identified, connected to each other through internet taking decisions independently. All networks and technologies of communication are used in building the concept of the internet of things such technologies are mobile computing, RFID, wireless sensors networks, and embedded systems, in addition to many algorithms and methodologies to get management processes, storing data, and security issues. IoT requires standardized approach for architectures, identification schemes, protocols and frequencies will happen parallels, each one targeted for a particular and specific use. by the internet of things many smart applications becomes real in our life , which enable us to reach and contact with every things in addition to facilities many important aspects for human life such as smart healthcare, smart homes, smart energy , smart cities and smart environments. Internet of things may facing two major challenges in order to guarantee seamless network access; the first issue relates to the fact that today different networks coexist and the other issue is related to the big data size of the IoT. Other current issues, such as address restriction, automatic address setup, security functions such as authentication and encryption, and functions to deliver voice and video signals efficiently will probably be affected in implementing the concept of the internet of things but by ongoing in technological developments these challenges will be overcome. The internet of things promises future new technologies when related to cloud, fog and distributed computing, big data, and security issues. By integrating all these issues with the internet of things, smarter applications will be developed as soon. This paper surveyed some of the most important applications of IoT with particular focus on what is being actually done in addition to the challenges that facing the
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implementation the internet of things concept, and the other future technologies make the concept of IoT feasible.
AUTHORS BIOGRAPHY Zainab Kamal Aldein Mohammed, received her B.Sc. (Honor) degree in Electrical Engineering, Telecommunication in 2010, and presently doing her M.Sc. degree in telecommunication. She worked as a network engineering under TETRA project in Sea Port Corporation for 6 years; she published research papers in heterogeneous network, her areas of research interest included routing protocols and heterogeneous network ,MANET, VANET and IOT. Elmustafa Sayed Ali Ahmed, he received his M.Sc. degree in electronics engineering, Telecommunication in 2012, and B.Sc. (Honor) degree in electrical engineering, Telecommunication in 2008. He was a wireless networks (Tetra system, Wi-Fi and Wi-Max, and CCTV) engineer in Sudan Sea Port Corporation for five years and a head department of electrical and electronics engineering, faculty of engineering in Red Sea University for one year. He published papers, book chapters, and books in wireless communications, computer and networking in peer reviewed academic international journals. His areas of research interest include, routing protocols, MANETs, mobile networks, VANETs, image processing, and cloud computing.
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[77] Dr. N. Preethi. Performance Evaluation of IoT Result for Machine Learning. Transactions on Engineering and Sciences, Vol. 2, Issue 11, November 2014 [78] Hele-Mai Haav. linked data connections with emerging information technologies: A survey. International Journal of Computer Science and Applications Vol. 11, No. 3, (2014) 21-44. [79] Rebecca Sawyer. The Impact of New Social Media on Intercultural Adaptation. University of Rhode Island, 2011. http://digitalcommons.uri.edu/cgi/viewcontent.cgi? article=1230&context=srhonorsprog [80] Odulaja, G.O., Security issues in internet of the things. Computing, Information Systems, Development Informatics & Allied Research Journal, Vol. 6, No. 1, March 2015. [81] P. Saichaitanya1, N. Karthik, D. Surender. Recent trends in IoT. International Journal of Electrical and Electronics Engineering, Vol. 8, Issue 2, December 2016. [82] Shahid Raza. Lightweith security solutions for the internet of things. Mälardalen University Press Dissertations, 2013. http://www.divaportal.org/smash/get/diva2:619066/FULLTEXT02 [83] Jaydip Sen. Security and privacy issues in cloud computing. Innovation Labs, Tata Consultancy Services Ltd., Kolkata, India. https://arxiv.org/ftp/arxiv/papers/1303/1303.4814.pdf [84] http://www.cloud-council.org/deliverables/CSCC-Cloud-Security-Standards-What-toExpect-and-What-to-Negotiate.pdf [85] https://www.ntia.doc.gov/files/ntia/publications/marcus_response_to_iot_rfc_rin_0660x c024__0.pdf [86] Paula Fraga-Lamas, et al., A Review on Internet of Things for Defense and Public Safety. Sensors (Basel) 2016 Oct., 16(10), 1644. [87] Virendra Dilip Thoke. Theory of distributed computing and parallel processing with applications, advantages and disadvantages. International Journal of Innovation in Engineering, Researchand Technology. http://www.ijiert.org/admin/papers/ 1452798652_ICITDCEME%E2%80%9915.pdf [88] https://www.rtinsights.com/what-is-fog-computing-open-consortium/
( Received 22 January 2017; accepted 08 February 2017 )
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LIBRARY INSTRUCTION IN A CLOUD: PERSPECTIVES FROM THE TRENCHES
OCLC Systems & Services: International digital library perspectives Library instruction in a cloud: perspectives from the trenches Regina Koury, Spencer J. Jardine,
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To cite this document: Regina Koury, Spencer J. Jardine, (2013) "Library instruction in a cloud: perspectives from the trenches", OCLC Systems & Services: International digital library perspectives, Vol. 29 Issue: 3, pp.161-169, https://doi.org/10.1108/ OCLC-01-2013-0001 Permanent link to this document: https://doi.org/10.1108/OCLC-01-2013-0001 Downloaded on: 03 April 2019, At: 17:50 (PT) References: this document contains references to 22 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 894 times since 2013*
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Library instruction in a cloud: perspectives from the trenches
Library instruction in a cloud
Regina Koury and Spencer J. Jardine Eli M. Oboler Library, Idaho State University, Pocatello, Idaho, USA
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Abstract Purpose – Cloud computing flexibility has advantages for IT professionals as well as non-technical users. This paper aims to look at cloud computing from the library instruction perspective. The authors aim to discuss types of cloud computing applications for organizing information and sharing content, creating tutorials, collaboration, scheduling and storage. Additionally, the paper seeks to discuss types of applications used at ISU for library instruction and implications for teaching. Design/methodology/approach – The authors conducted a literature review followed by practical applications of library instruction that included cloud-computing technologies. Findings – The paper encourages library professionals to take advantage of cloud computing applications to provide better library instruction. Originality/value – This paper offers insights on how cloud computing can be used for library instruction.
161 Received 4 January 2013 Revised 8 February 2013 Accepted 8 February 2013
Keywords Library instruction, Cloud computing, Libraries, United States of America, Digital libraries, Learning Paper type Research paper
Introduction Google Apps, Zoho, and LiveBinders have one thing in common: these free or inexpensive software applications use cloud computing to help educators and students be innovative, collaborative and access information ubiquitously. What is cloud computing? “Cloud computing” refers to the use of computing resources on the internet instead of on individual personal computers. The field is expanding and has significant potential value for educators (Aaron and Roche, 2011). The concept of cloud computing has been around for a while and is defined in information technology (IT) terms as a style of computing where massively scalable IT-enabled capabilities are delivered as a service to external customers using internet technologies (Plummer et al., 2008). Cloud computing can be divided into three categories: Software-as-a-Service (SaaS), Platform-as-a-Service (PaaS), and Infrastructure-as-a-Service (IaaS) (Mitchell, 2010). The National Institute of Standards and Technology, Information Technology Laboratory defines those three categories as following: SaaS applications are accessible from various client devices through a thin client interface such as a web browser (e.g. LibQuides). A thin client is a low-cost, centrally managed computer devoid of CDROM players, diskette drives, and expansion slots (Crider and Farmer, 2007). PaaS capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider (e.g. Drupal). IaaS capability provides processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications (e.g. Amazon Elastic Compute Cloud or Amazon EC2). In all
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three models the consumer does not manage or control the underlying cloud infrastructure (Mell and Grance, 2011). Examples of cloud computing include Google Suite of Apps (Drive, Slide Rocket, Calendar, Sites, etc.), Flickr, YouTube, Zotero, and other free or inexpensive applications with ubiquitous access, as well as collaboration and sharing abilities. Increasingly, library vendors have been offering services in the cloud. For instance, Integrated Library Systems (ILS) in the cloud: ExLibris’s Alma and OCLC’s Web – Scale management services offer a web-based library management suite of tools for, acquisitions, circulation, license management and workflow, and metadata management. Horizon Report 2009 described features of cloud computing as having e-mail, word processing, spreadsheets, presentations, collaboration, media editing, and more inside a web browser, while the software and files are simultaneously housed in the cloud. It does not matter where the work is stored; what matters is that information is accessible 24/7. Cloud computing has been embraced by both the K-12 community and higher education, with university professors using these resources to enhance education (Blue and Tirotta, 2011). Many universities and public schools throughout the US migrated to cloud computing applications, either to Google (Utah State University) or Microsoft Live@edu (City University of New York). Some had done so to save money, while others wanted to enhance patron’s educational experience (Korzenowski, 2011). Librarians have been at the forefront, adopting Web 2.0 technologies and subsequently cloud computing applications. Teaching in the cloud was shown to offer a new and better tool for teaching and collaboration (Pang, 2009). Constructivism and cooperative learning are enhanced through cloud technologies. Cloud applications contain tools that support activities for accessing prior knowledge such as retrieving and sharing information (Denton, 2012). These applications have been used in libraries in various ways. A literature review found articles on using Google Apps to design library instruction sessions and assignments/quizzes (Martin et al., 2009; Booth, 2011; Denton, 2012; Simpson, 2012), utilizing VoiceThread to encourage participation and collaboration in library instruction (Ditkoff and Young, 2011), leveraging Ning for asynchronous training (Deeds et al., 2011), sharing files with Dropbox (Bagley, 2011), and hosting content via LibGuides (Reiman-Sendi et al., 2011). This article will summarize the use of an exponentially growing number of cloud computing applications in library instruction. Library instruction in the Cloud Idaho State University (ISU) is a Carnegie-classified research university-high institution located in Pocatello, Idaho, with annual enrollments of approximately 14,500 students. ISU offers more than 280 programs in the allied health professions, natural and physical sciences, humanities, performing and visual arts, education, engineering, business, and technology. ISU instruction librarians asked how we could be creative and innovative in the classroom, without having to re-invent the wheel. We considered some of the following applications and their functionality as legitimate solutions. Organizing information and sharing content Sites such as VoiceThread (http://voicethread.com/), LiveBinders (www.livebinders. com/), Scoop.It (www.scoop.it/), Google Sites (sites.google.com/) or Wikispaces (www.
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wikispaces.com/) allow for organization of information in a variety of formats, using various page layouts and various features like audio, sound files, graphic images, etc. For example, LiveBinders allows users to organize information in an online binder through a series of tabs. Information can be organized as web site links or files, all in one place for easy, accessible sharing. The creator of the binder can restrict who has viewing access as well as set editing privileges to the binder. Delicious bookmarking software works well for sharing online content, while Flickr (www.flickr.com/), Picasa (picasa.google.com/), SmugMug (www.smugmug.com/), Pinterest (pinterest.com/), Photobucket (photobucket.com/) facilitate sharing digital images. For institutions that may not have the resources to pay for LibGuides, Google Sites can be a useful tool, where class-specific resources can be highlighted to assist students with their assignments. Google Sites allows users to create a web site with a template or from scratch. The process is intuitive and has a low learning curve. At ISU we created several class-specific Google Sites for students in specific classes (e.g. Spanish 3381). Links to web sites and tutorials provided the information they needed in order to understand which databases to use, as well as titles of reference resources and contact information (see Figure 1). The more the site is tailored to the students’ needs, i.e. their assignment requirements, the more likely they are to utilize it. We created a Google Site for a political science class that was simulating a session in Congress. Students were given an opportunity to assume the identity of representatives of the House of Representatives, so they needed to learn about these politicians, their campaigns, campaign finances, committee work, and so forth. The Google Site for this class offered
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Figure 1. Google sites page
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titles of reference resources about the members of Congress, plus several links to online resources that would aid them in their assignment, as well as librarians’ contact information. Google Sites allows students to seek out and find instruction when they need it. For instance, a month after the library instruction sessions, one student asked if the Site would be deleted any time soon; he expressed interest in its survival, since he had been using it regularly as a launching point to access important resources to complete his class assignments. Students can access online screencasts embedded within the Site to better understand how to use the resources highlighted by librarians. Asynchronous instruction suits the needs of many students, allowing them to manage their own time. Likewise, linking to multimedia tutorials and web sites expands instruction librarians’ options. Again, librarians do not need to re-invent the wheel and create tutorials that have already been created. Instead, point to other experts who are sharing their knowledge via the virtual world. The Google platform permits the Sites manager to designate who can view, edit, and own the Sites. Additionally, the owner may determine whether or not the Sites are shared publicly on the web or only to specific persons. Institutions that have adopted the Google applications have the option of making their Sites available only to those at their institution. Therefore, instructors can then add items to the Sites that they wish for their own students to use exclusively. Turning to a different tool, a wiki was used to teach two sections of a sports science class studying disabilities and diseases as they related to their field. The health sciences librarian, who had the expertise, created the wiki page and focused only on those resources directly relevant to the students’ assignment as determined jointly by the librarians. The sports science librarian focused on the basic search strategies – including Boolean operators in search statements, thinking of synonyms, narrowing a search, broadening a search, truncating keywords, etc. He emphasized the importance of developing basic search strategies, explaining that these skills transfer from one database to another. The wiki page was created before the class, and the link was shared with students via a printed sticker, attached to the backside of a business card for easy reference. Feedback from the students indicated that they appreciated the expert knowledge shared by each of the librarians. Creating tutorials Screencast-O-Matic (www.screencast-o-matic.com/), Vimeo (vimeo.com/), YouTube (www.youtube.com/), SnagIt (www.techsmith.com/Snagit), Jing (www.techsmith.com/ jing.html), and Slide Rocket (www.sliderocket.com/) are some of the cloud technologies to help with creating presentations and tutorials. Slide Rocket, a Google online presentation platform, lets users create, manage, share and measure presentations fast and offers an easy process for creating tutorials. Using Microsoft PowerPoint slides, the user can add audio, graphics, flash, and also review statistics on who, where, and when presentations were viewed as well as how viewers interacted with them. Collaboration Google has been on the forefront of cloud computing with its apps to make collaboration and communication easier. Google Apps, particularly Drive (formerly Documents), Slide Rocket, and Calendar provide a paradigm shift in ubiquitous access. Google Drive has been used for information literacy assessment because of its
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collaborative nature, since it allowed for sharing findings with faculty to address weak areas of information literacy skills (Hsieh and Dawson, 2010). One can create or import spreadsheets from a variety of formats, with the resulting document saved on the Google servers. Google Drive automatically saves open documents to prevent data loss and maintains a limited history of revisions. Up to 50 individuals can edit a spreadsheet simultaneously, enabling all members of a research group to edit their data in real time. Finally, Google Drive readily allows the creation of Web forms that make it simple to control access by different groups of students, which was a deciding factor in choosing Google Drive. Google spreadsheets can be used to share instruction statistics or schedule an event, even allowing students to a sign up for library tours. Also, a revision history exists so that one can quickly compare and restore previous versions while simultaneously seeing who is responsible for specific revisions. At ISU we used Google Drive and Google Spreadsheets in collaboration with multiple first year seminar classes. One instruction librarian created a list of books, subject headings, and keywords used to locate materials on cognitive impairments, while the other supplemented that list with examples of databases that would be useful in searching for online articles (see Figure 2). Because both instructors felt comfortable sharing responsibilities, preparation time for the class was cut in half for each librarian, allowing for a more efficient workflow. We also shared Google Drive and Spreadsheet with a faculty instructor, who added information to the documents she thought might be useful.
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Scheduling events Doodle (www.doodle.com/), is a free, no-registration-required platform, which allows anyone to schedule a meeting by creating a poll of available dates and times. If users
Figure 2. Google spreadsheet page
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create a free account, the service will allow them to track already-created polls, choose time zones, and send automated notifications when participants have completed the poll. As a library instructor scheduling individual meetings with students or faculty via e-mail can be a challenge. ISU librarians use Doodle, since it allows setting up dates and times when a library instructor is available for consultation, thus giving a choice for everyone to meet and additionally lets a library instructor track personal communication with students or faculty. With Doodle users can connect Google accounts (or Microsoft Outlook) in order to make use of calendars when creating a poll or when participating in one. Additionally, users can let Doodle synchronize the dates and times that they choose in one of their calendars. Furthermore, users can select e-mail addresses directly from their Google Contacts if they send invitations via Google Calendar (www.google.com/calendar/), which also permits collaborative scheduling of events. At ISU we use Google calendar to schedule faculty meetings, instruction, training, and other events, because we all have access to it and can check any time. Storage Dropbox (www.dropbox.com/) is one of the more popular cloud-computing services. This free online service lets users access and store photos, documents, and videos ubiquitously, making it easier to share them electronically with anyone. It lets users upload multiple files at a time and tracks each upload with understandable time stamps. Shared folders allow people to work together on the same projects and documents. Additionally, Dropbox is available as an app for iPhone, iPad, Kindle Fire, Android, and BlackBerry. This free service has quintupled its storage capacity from 5 million in 2010 to 25 million from 175 countries in 2011, storing files at a rate of 300 million per day (Fielding, 2011). They now house an amazing 100 billion files. Some other sites that provide file storage include Google Drive, ThinkFree Office, ADrive, Amazon Cloud Drive, Box, Mozy, Windows Live SkyDrive, and ZumoDrive. Many file-hosting sites offer some storage free and charge for greater capacity. Surveys, evaluations Google Forms is an online survey creation tool. Instead of sending e-mails, requesting information, and then manually collecting and organizing responses, Google does all the work. Data are placed directly into a spreadsheet and charts are provided. This results in easy and efficient data analysis. In addition, this feature can be used to create an online quiz for students. A link to the survey can be sent to students through e-mail or shared during class; the survey can challenge the “‘IAKT Syndrome’ – or the ‘I already know this’ syndrome (Ross and Furno, 2011). Much as some instructors use classroom response systems or “clickers,” surveys can gather immediate feedback, affording an opportunity for library instructors to provide more relevant and needed instruction. Students appreciate the anonymous nature of the surveys, and results appear in real time, making it easy to engage students in a discussion of the outcome or adapt the direction of the instruction. Pre- and post-tests can find out what students already know or what they learned in class. Besides, they can add an element of fun (Eva and Nicholson, 2011). Google Forms have been used for gathering instruction-related feedback, including instruction evaluations. Other popular web-based survey software are Poll Daddy (http://polldaddy.com/) SurveyMonkey (www.surveymonkey.com/) and Zoomerang (www.zoomerang.com/).
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Conclusion As an ongoing paradigm shift, the potential of library instruction in the cloud is exciting and expanding. No matter what the discipline, learning activities such as discussion, peer review, collaborative writing, team projects, and reflective journals can be pursued online (Aaron and Roche, 2011). One of the standards from the National Educational Technology Standards (NETS) for teachers urges teachers to continuously improve their professional practice, model lifelong learning, and exhibit leadership in their school and professional community by promoting and demonstrating the effective use of digital tools and resources. Cloud computing tools can enhance engagement among students, educators, and researchers in a cost-effective manner (Thomas, 2011). Instruction librarians at Idaho State University have found cloud computing applications to be useful and plan on continued usage of these technologies in the future. Cloud computing sites bibliography: ADrive: www.adrive.com/ Amazon Cloud Drive: www.amazon.com/gp/feature.html?ie ¼ UTF8&docId ¼ 1000828861 Box: www.box.com/ Flickr: www.flickr.com Google Apps: www.google.com/intl/en/about/products/ Jing: www.techsmith.com/jing.html LibGuides: http://springshare.com/libguides/ LiveBinders: www.livebinders.com Mozy: http://mozy.com/ Picasa: picasa.google.com/ Pinterest: http://pinterest.com/ Photobucket: photobucket.com/ Poll Daddy: http://polldaddy.com/ Scoop.It: www.scoop.it/ Screencast-O-Matic: www.screencast-o-matic.com/ Slide Rocket: www.sliderocket.com/ SmugMug: www.smugmug.com/ SnagIt: www.techsmith.com/Snagit SurveyMonkey: www.surveymonkey.com/ ThinkFree Office: www.thinkfree.com/ Wikispaces: www.wikispaces.com/ Windows Live SkyDrive: http://windows.microsoft.com/en-US/skydrive/download YouTube: www.youtube.com/ Vimeo: vimeo.com/
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VoiceThread: http://voicethread.com/ Zoho: www.zoho.com/ Zoomerang: www.zoomerang.com/ ZumoDrive: www.zumodrive.com/ References Aaron, L.S. and Roche, C.M. (2011), “Teaching, learning, and collaborating in the cloud: applications of cloud computing for educators in post-secondary institutions”, Journal of Educational Technology Systems, Vol. 40 No. 2, pp. 95-112. Bagley, C. (2011), “Parting the clouds: use of Dropbox by embedded librarians”, in Corrado, M. and Moulaison, H. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman Publishers, New York, NY, pp. 159-164. Blue, E. and Tirotta, R. (2011), “The benefits and drawbacks of integrating Cloud Computing and interactive whiteboards in teacher preparation”, Tech Trends: Linking Research & Practice to Improve Learning, Vol. 55 No. 3, pp. 31-39. Booth, C. (2011), Reflective Teaching, Effective Learning: Instructional Literacy for Library Educators, American Library Association, Chicago, IL. Crider, J. and Farmer, J. (2007), “Lamar County library system’s experience with Thin-Client Computing”, Mississippi Libraries, Vol. 71 No. 1, pp. 20-23. Deeds, L., Kissel-Ito, C. and Knox, A. (2011), “Ning, fostering conversations in the Cloud”, in Corrado, M. and Moulaison, H. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman Publishers, New York, NY, pp. 181-186. Denton, D. (2012), Enhancing instruction through constructivism, cooperative learning, and Cloud Computing, Techtrends: Linking Research & Practice To Improve Learning, Vol. 56 No. 4, pp. 34-41. Ditkoff, J. and Young, K. (2011), “Speak up! Using VoiceThread to encourage participation and collaboration in library instruction”, in Corrado, M. and Moulaison, H. (Eds), Getting Started with Cloud Computing: A LITA Guide, Neal-Schuman Publishers, New York, NY, pp. 191-197. Eva, N. and Nicholson, H. (2011), “Do get technical! Using technology in library instruction WILU 2011”, Regina, SK, Partnership: The Canadian Journal of Library & Information Practice & Research, Vol. 6 No. 2, pp. 1-9. Fielding, C. (2011), “At Dropbox, over 100 billion files served – and counting”, Gigaom. Tech News and Analysis, available at: http://gigaom.com/2011/05/23/at-dropbox-over-100billion-files-served-and-counting/ (accessed 20 June 2012). Hsieh, M.L. and Dawson, P.H. (2010), “A university’s information literacy assessment program using Google Docs”, Brick and Click Libraries Proceedings of an Academic Library Symposium in Maryville, MO November 5, 2010, available at: www.nwmissouri.edu/ library/brickandclick/previous/proceedings2010.pdf#page¼128 (accessed 20 June 2012). Korzenowski, P. (2011), Profiles of the Use of Cloud Computing in Higher Education, Primary Research Group, New York, NY. Martin, A., Snowden, K. and West, D. (2009), Are You Ready for “The Cloud”? Implications and Uses of Cloud Computing for Libraries, Brick and Click Libraries, Maryville, MO, available at: http://d-scholarship.pitt.edu/5838/1/ED507380.pdf#page¼73 (accessed 20 June 2012). Mell, P. and Grance, T. (2011), The NIST Definition of Cloud Computing, National Institute of Standards and Technology, US Department of Commerce, Washington, DC, available at:
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http://csrc.nist.gov/publications/drafts/800-145/Draft-SP-800-145_cloud-definition.pdf (accessed 20 June 2012). Mitchell, E. (2010), “Using cloud services for library IT infrastructure”, code{4}lib Journal, No. 9, available at: http://journal.code4lib.org/articles/2510 (accessed 20 June 2012). Pang, L. (2009), “A survey of Web 2.0 technologies for classroom learning”, International Journal of Learning, Vol. 16 No. 9, pp. 743-759. Plummer, D.C., Bittman, T.J., Austin, T., Cearley, D.W. and Smith, D.M. (2008), Cloud Computing: Defining and Describing an Emerging Phenomenon, available at: www.emory.edu/ BUSINESS/readings/CloudComputing/Gartner_cloud_computing_defini ng.pdf (accessed 20 June 2012). Reiman-Sendi, K., Varnum, K. and Bertram, A. (2011), “Keeping your data on the ground when putting your (Lib)Guides in the Cloud”, in Corrado, M. and Moulaison, H. (Eds), Getting Started with Cloud Computing: a LITA Guide, Neal-Schuman Publishers, New York, NY, pp. 153-158. Ross, A. and Furno, C. (2011), “Active learning in the library instruction environment: an exploratory study”, Portal: Libraries and the Academy, Vol. 11 No. 4 pp. 953-970. Simpson, S.R. (2012), “Google Spreadsheets and real-time assessment instant feedback for library instruction”, College & Research Libraries News, Vol. 73 No. 9, pp. 528-549. Thomas, P.Y. (2011), “Cloud computing: a potential paradigm for practising the scholarship of teaching and learning”, Electronic Library, Vol. 29 No. 2, pp. 214-224. Further reading Johnson, L., Levine, A. and Smith, R. (2009), The 2009 Horizon Report, The New Media Consortium, Austin, TX. About the authors Regina Koury is a Head of Collection Management at the Idaho State University library since August 2012, where she had worked for the past four years as an Electronic Resources Librarian. Her previous work at University of Southern California involved interlibrary loan, serials, acquisitions, and electronic resources. Regina received MLIS from University of Pittsburgh in 2008, and Med in Instructional Technology from Idaho State University in 2012. Regina Koury is the corresponding author and can be contacted at: [email protected] Spencer J. Jardine works as the Coordinator of Instruction in Idaho State University’s Eli M. Oboler Library. Spencer teaches a one-credit, information-literacy course as well as many library instruction sessions to undergraduate and graduate students in multiple disciplines. He graduated with a Master of Arts degree from the University of Iowa’s School of Library and Information Science in 2007. Additionally, he earned a Master of Arts degree in humanities from Brigham Young University in 2005.
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This article has been cited by: 1. James J. Thull. Librarians and the Evolving Research Needs of Distance Students 203-216. [Crossref] 2. ParriginJames, James Parrigin. 2017. From request to assess: using cloud-based tools for the library instruction lifecycle. Library Hi Tech News 34:6, 14-20. [Abstract] [Full Text] [PDF] 3. De SarkarTanmay, Tanmay De Sarkar. 2017. Adopting a photo-sharing site as a library tool: a web-based survey. Information and Learning Science 118:3/4, 185-209. [Abstract] [Full Text] [PDF] 4. Raymond Pun. 2016. The library and the academic resource center. Digital Library Perspectives 32:1, 31-40. [Abstract] [Full Text] [PDF] 5. Sarah Cisse. Practical Tips for Successful One-Shot Instruction 119-143. [Crossref] 6. James Thull. Distance Educators and Librarians 1502-1512. [Crossref] 7. Barbara Blummer, Jeffrey M. Kenton. 2015. Utilizing Web 2.0 Technologies for Library Web Tutorials: An Examination of Instruction on Community College Libraries' Websites Serving Large Student Bodies. Community & Junior College Libraries 21:3-4, 101-124. [Crossref] 8. James Thull. 2015. Distance Educators and Librarians. International Journal of Innovation in the Digital Economy 6:3, 33-45. [Crossref]
LONG-TERM PRESERVATION OF BIG DATA: PROSPECTS OF CURRENT STORAGE TECHNOLOGIES IN DIGITAL LIBRARIES
Library Hi Tech Long-term preservation of big data: prospects of current storage technologies in digital libraries Wasim Ahmad Bhat,
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Article information: To cite this document: Wasim Ahmad Bhat, (2018) "Long-term preservation of big data: prospects of current storage technologies in digital libraries", Library Hi Tech, Vol. 36 Issue: 3, pp.539-555, https://doi.org/10.1108/ LHT-06-2017-0117 Permanent link to this document: https://doi.org/10.1108/LHT-06-2017-0117 Downloaded on: 03 April 2019, At: 17:49 (PT) References: this document contains references to 54 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 1079 times since 2018*
Users who downloaded this article also downloaded: (2018),"Digital preservation at Big Data scales: proposing a step-change in preservation system architectures", Library Hi Tech, Vol. 36 Iss 3 pp. 524-538 https://doi.org/10.1108/LHT-06-2017-0122 (2018),"A Big Data smart library recommender system for an educational institution", Library Hi Tech, Vol. 36 Iss 3 pp. 498-523 https:// doi.org/10.1108/LHT-06-2017-0131 Access to this document was granted through an Emerald subscription provided by emeraldsrm:434496 []
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About Emerald www.emeraldinsight.com Emerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services. Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. *Related content and download information correct at time of download.
The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0737-8831.htm
Long-term preservation of big data: prospects of current storage technologies in digital libraries Wasim Ahmad Bhat
Prospects of current storage technologies
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Department of Computer Sciences, University of Kashmir, Srinagar, India
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Abstract Purpose – The purpose of this paper is to investigate the prospects of current storage technologies for long-term preservation of big data in digital libraries. Design/methodology/approach – The study employs a systematic and critical review of the relevant literature to explore the prospects of current storage technologies for long-term preservation of big data in digital libraries. Online computer databases were searched to identify the relevant literature published between 2000 and 2016. A specific inclusion and exclusion criterion was formulated and applied in two distinct rounds to determine the most relevant papers. Findings – The study concludes that the current storage technologies are not viable for long-term preservation of big data in digital libraries. They can neither fulfil all the storage demands nor alleviate the financial expenditures of digital libraries. The study also points out that migrating to emerging storage technologies in digital libraries is a long-term viable solution. Research limitations/implications – The study suggests that continuous innovation and research efforts in current storage technologies are required to lessen the impact of storage shortage on digital libraries, and to allow emerging storage technologies to advance further and take over. At the same time, more aggressive research and development efforts are required by academics and industry to further advance the emerging storage technologies for their timely and swift adoption by digital libraries. Practical implications – The study reveals that digital libraries, besides incurring significant financial expenditures, will suffer from potential loss of information due to storage shortage for long-term preservation of big data, if current storage technologies are employed by them. Therefore, policy makers and practitioners should meticulously choose storage technologies for long-term preservation of big data in digital libraries. Originality/value – This type of holistic study that investigates the prospects of magnetic drive technology, solid-state drive technology, and data-reduction techniques for long-term preservation of big data in digital libraries has not been conducted in the field previously, and so provides a novel contribution. The study arms academics, practitioners, policy makers, and industry with the deep understanding of the problem, technical details to choose storage technologies meticulously, greater insight to frame sustainable policies, and opportunities to address various research problems. Keywords Digital libraries, Digital preservation, Big data, Data reduction, Flash technology, Magnetic storage Paper type Research paper
1. Introduction Long-term preservation of digital information strives to protect valuable information for access by present and future generations. Digital libraries include long-term digital preservation as one of their core functions. Digital initiatives across the globe have led to digitisation of millions of manuscripts, periodicals, and other resources including audio and video. Various governments are supporting the shift to digital records and preservation (Adu et al., 2016; Adu and Ngulube, 2016). With the emergence of big data, digital libraries have become an increasingly significant area of research. Big data characteristics significantly differ from those of traditional digital data in many ways. Big data has myriad and manifold sources, and holds immense potential to deliver new knowledge and greater insights (Foster, 2016). Digital libraries generally receive big data from researchers. Big data submitted by researchers is the result of the research work conducted by them across a wide variety of subjects (Han, 2015), which is collected at atomic, molecular, geological, and astronomical level. Digital libraries preserve the submitted research data for long term so as
Received 24 June 2017 Revised 16 September 2017 18 November 2017 23 November 2017 25 November 2017 Accepted 25 November 2017
Library Hi Tech Vol. 36 No. 3, 2018 pp. 539-555 © Emerald Publishing Limited 0737-8831 DOI 10.1108/LHT-06-2017-0117
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to allow its continuous mining (Seadle, 2016), and varied future analysis (He and Nahar, 2016) by researchers across the globe (Beyene, 2017). Unfortunately for digital libraries, big data is huge in volume (Bhat and Quadri, 2015), and thus requires very large capacity digital libraries for its long-term preservation. Digital libraries that can fulfil all the storage demands of long-term preservation of big data in a cost-effective way rely on the potential of storage technologies to do so. These storage technologies must offer huge capacity with minimal storage cost/bit and should incur less infrastructure, maintenance, and operational costs. This implies that as big data grows they also need to progress both technologically and economically to cope up with the challenge. Unfortunately, in this era of big data, the rate of production of data has significantly outpaced the growth of storage capacity of current storage technologies. As an example, in 2013, the available storage capacity could hold just 33 per cent of the digital universe, and by 2020, it will be able to store less than 15 per cent (Turner et al., 2014). Undoubtedly, there is a growing gap between the volume of big data created and the extent of available storage capacity to preserve it. In fact, by 2020, a minimum storage shortage of over six zettabytes (ZBs) is anticipated by Seagate, as shown in the Figure 1. While the generation of big data is encouraged for the value it promises, it significantly challenges current storage technologies to cope up with the preservation of huge volumes of the data. Unfortunately, digital libraries are not exonerated from the implications of this storage shortage. In fact, it significantly challenges the potential of digital libraries to fulfil their core task of long-term preservation of big data, and thus questions the prospects of current storage technologies in digital libraries. Therefore, academics and practitioners alike are confronted by one important question, which is: RQ1. What are the prospects of current storage technologies for long-term preservation of big data in digital libraries? Answering this question can arm policy makers, practitioners, academics, and industry with deep understanding of the problem, and knowledge to choose appropriate storage technologies, make right decisions and frame sustainable policies for long-term preservation of big data in digital libraries. This paper attempts to answer this question by employing a systematic and critical review of the relevant literature to identify the challenges, limitations, and advances of current storage technologies and investigates their prospects in digital libraries. The rest of this paper is organised as follows. Section 2 introduces the research methodology adopted by the study. Section 3 investigates the technological limitations of
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magnetic drive technology, while Section 4 evaluates the competence of solid-state drive (SSD) technology to replace magnetic drive technology. Section 5 analyses the efficiency of data-reduction techniques, and Section 6 briefly discusses emerging storage technologies suitable for digital libraries. Finally, Section 7 presents the findings and recommendations of the study, and Section 8 presents the conclusions drawn from the study.
Prospects of current storage technologies
2. Research methodology Relevant research concerning challenges, advances, and prospects of magnetic storage technology, solid-state storage technology, and data-reduction techniques was identified by searching the online computer databases for primary research material. These online databases included ACM Digital Library, IEEEXplore, Web of Science, Sciencedirect, SpringerLink, Arxiv.org, Inspec, Scirus, Scopus, CiteSeerX, etc. They were searched for publication from 2000 to 2016. In order to ensure that relevant studies are not missed, the search keywords remained broad. The keywords were broadly categorised into 3 as shown in Table I. Also, many alternative keywords with similar meanings were used. Furthermore, a comprehensive search using same keywords was made for internet resources which yielded white papers, technical reports, etc. Due to the usage of broad keywords, the process yielded 241 related studies. Only those studies were included that satisfied the following inclusion criteria:
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i1: they were relevant to the search terms mentioned in Table I;
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i2: they were published between 2000 and 2016; and
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i3: they had undergone a standard peer-review.
The next step was a detailed examination of the papers, and following exclusion criteria were employed to reject a study for further consideration: •
e1: the challenges and limitations described were either obsolete or not applicable;
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e2: the advances discussed were either insufficient or not applicable; and
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e3: the accuracy and credibility of the content was questionable.
The bibliography of examined papers was also checked for further identification of relevant studies. Based on the filtering criteria, the following numbers of studies were excluded: 24 studies related to magnetic storage technology, 27 studies related to solid-state storage technology, and 27 studies related to data-reduction techniques. Furthermore, seven internet resources and five scholarly articles were discarded based on the content accuracy and credibility criteria. Also, 18 studies were excluded for being out-of-date. Finally, a total of 133 studies were included for the study. Figure 2 shows the outcome of the filtering criteria employed to identify the most relevant literature for the study. For a systematic and critical review of the literature, articles were first previewed for thematic organisation. After that, each article was read, questioned, and summarised. For each article, the challenges and limitations highlighted, and the advances described were recorded. With respect to literature related to challenges, limitations, and advances of Literature
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Table I. Terms and operators used to formulate search keywords
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magnetic storage technology, it was observed that studies discussed technological challenges more often than advances. This indicates that even though the magnetic storage technology has received significant advances over time, the technological challenges have always been a matter of concern. Furthermore, it was also observed that with the growing popularity of big data, both technological and economic aspects of magnetic storage technology have received significant focus. At the same time, literature pertaining to solid-state storage technology not only shows progress in the technology but also points towards the incompetence of the technology to replace magnetic storage technology. On the other hand, in case of data-reduction techniques, the literature is full with evidences that support their effectiveness. However, there exist studies that question their usage. Perspective repetition and repetition of research approach were also observed in many studies belonging to all the three categories of the included literature. Consequently, many challenges and limitations of storage technologies and reduction techniques emerged, which were categorised into three main categories: technological limitations of magnetic drive technology, competence and adoption of SSD technology, and efficiency and overheads of data-reduction techniques. Also, existence of literature suggesting adoption of emerging storage technologies for long-term preservation of big data was observed. 3. Technological limitations of magnetic drives Magnetic drives are generally employed for bulk storage. In digital libraries, the huge storage shortage for long-term preservation of big data can be largely attributed to the failure of the storage technology to cope up with the growth of big data. Considering the fact that it is far harder to manufacture capacity than it is to generate data, building factory capacity that is capable of meeting such a huge storage demand requires huge investment. This implies that the advances in magnetic drive technology are at the core of overcoming the depleting storage capacity in digital libraries. Since the storage shortage in digital libraries is eminent and growing, limitations of magnetic drive technology are to be blamed. This section investigates these technological limitations of magnetic drive technology to assess its prospects to overcome the problem of storage shortage in digital libraries. 3.1 Decline in growth of areal density Growth in areal density of magnetic drives is at the core of magnetic drive technology to fulfil all the storage demands of digital libraries. Magnetic drives have witnessed huge growth in areal density since their inception up to the recent past. Morris and Truskowski (2003) observed that since 1980 the areal density of magnetic drive has increased by seven orders of magnitude, while the cost has declined by five orders. In past, more such estimates and predictions have been made; the less familiar one being Storage law (Porter, 2005).
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3.2 Slowing down of cost drop Storage cost/bit is one of the major factors that decide the adoption and implementation of any storage technology in digital libraries. In fact, significant and continuous annual cost drop in magnetic drives made them the most favourable preservation media in the past. Grochowski and Halem (2003) observed that with a compound growth rate of 60-100 per cent per year in areal density of magnetic drives, the expected price declines averaged 37-50 per cent per year. Unfortunately, the prevailing growth rate of areal density is about 15-20 per cent per year. Worse, this does not translate directly into a 15-20 per cent per year drop in cost/bit (Rosenthal et al., 2012). In past, to maintain density growth of 40 per cent, magnetic drives with higher storage capacity included more platters per drive. In absence of any significant growth in areal density, this approach for capacity growth is expected to continue. Because adding platters adds cost, and only few more platters can be added without disruptive and expensive changes in 103 Areal density (Gbits/in2)
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The study observed that the capacity of magnetic drives then doubled after every nine months for at least a decade. Among these predictions and estimates, the most popular one is Kryder’s law. According to Kryder’s law, the progress in areal density of magnetic drives had been far better than the progress in semiconductor industry. The law suggested that the areal density of a magnetic drive doubles approximately after every two years. It forecast an improvement of 40 per cent per year in areal density of magnetic drives (Walter, 2005). This improvement directly translated into 40 per cent per year reduction in cost/bit (Hoagland, 2003; Goda and Kitsuregawa, 2012). Consequently, it was predicted that a two-platter, 2.5-inch magnetic drive would store approximately 40 terabytes (TBs) and cost about $40 in year 2020. However, the prediction was questioned halfway into the forecast period, as reflected in Figure 3. Unfortunately, there is no golden yardstick in magnetic drive technology which can perfectly (or near perfectly) predict the growth of areal density of magnetic drives. The current prediction of Kryder’s law suggests less than 20 per cent per year improvement in areal density (Kryder and Kim, 2009; Rosenthal et al., 2012). This implies that to reach a capacity of 20 TBs per drive by 2020 would require a consistently high growth rate of better than 80 per cent per year, and that too should had been there since year 2014. Unfortunately, such an improvement in growth of areal density is far from being practically possible. Consequently, since the number of magnetic drives shipped per year does not change much, no significant progress in areal density means no significant change in the annual storage capacity produced. This implies that the storage shortage in digital libraries will increase even when the growth in areal density remains stagnant. Since growth in areal density of magnetic drives is expected to further decline and big data is expected to grow more aggressively, the storage shortage will also increase aggressively to jeopardise long-term preservation of big data in digital libraries.
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Figure 3. Growth of areal density of magnetic drives (1991-2015)
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the drive form factor, future improvements in storage cost/bit will be much slower than in the past, as shown in Figure 4 (Rosenthal, 2017). In addition to this, adding platters adds more energy consumption and cooling costs. It can be safely argued that slowing down of cost drop of magnetic drives has rendered the technology economically less lucrative for digital libraries. 3.3 Higher energy-consumption costs Energy efficiency has been identified as one of the primary challenges of organisations (Minas and Ellison, 2009) with digital libraries being no exception. In recent years, energy efficiency of data centres has become a matter of concern for its economic (Dayarathna et al., 2016) as well as environmental implications (Whitehead et al., 2014). The energy costs of powering a typical data centre double every five years resulting into power bills becoming a significant expense for today’s data centres (Poess and Nambiar, 2008; Gao et al., 2013). According to a statement issued by the US Environmental Protection Agency, data centres in the USA alone consumed a significant amount of energy, accounting for 1.5 per cent of the total US electricity consumption in 2010 at a cost of $4.5 billion annually (Gu et al., 2014). Natural Resources Defence Council reported that US data centres in total used 77 billion KWhr of electrical energy in 2011 (see Table II), and 91 billion KWhr in 2013 (NRDC, 2015). At the time when IT organisations have identified energy efficiency as one of their primary challenges, magnetic drives continue to be very energy inefficient. Servers and storage devices consume about 26 per cent of the energy consumed by a data centre while as cooling infrastructure consumes about 50 per cent (Dayarathna et al., 2016). Magnetic drives consume energy in both idle and operating states and require cooling systems to dissipate the heat generated by them (Hylick et al., 2008). Therefore, magnetic drives, even in idle state, have a direct impact on energy consumed by the cooling infrastructure. In addition to this, the limited lifetime of magnetic drives requires data migration every two or three years to avoid any data loss (Hughes et al., 2002). This significantly increases the energy-consumption burden since a considerable amount of energy is consumed during such data migration. Without a doubt, magnetic drives pay no regards to energy efficiency and do have a direct impact on cooling The actual data points (1990-2015) Linear fit to data points (1990-2015) Industry projections (2015-2020)
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3.4 Limitations imposed by super-paramagnetism Digital storage technologies eventually encounter physical phenomena that limit their further progress. Super-paramagnetism is one such phenomenon that sets a limit on the storage density of magnetic drives. As magnetic particles on the platter get smaller, the temperature below which they can retain information for a given time decreases. When bits are too small, thermal fluctuations can easily flip the direction of magnetisation in each bit that results into permanent loss of information. For the temperatures and times involved in magnetic drives, this is expected to limit bit densities to well under 100 terabits per square inch (Shiroishi et al., 2009). Since magnetic drive technology is an old technology and has witnessed significant progress in the past, the technology is expected to face the limit soon. At the current roadmap of 20 per cent per year density increase, this limit could be encountered as soon as 2030; at 40 per cent (as used by Kryder and Kim, 2009) it would be encountered sometime after 2022. The inevitability of super-paramagnetic limit with progressive increase in areal density is illustrated in Figure 5 (Koelmans et al., 2015). Certainly, a physical phenomenon will finally restrict the growth in areal density and cost drop of magnetic drives rendering them obsolete within the next decade, or so. Hence, magnetic drives are surely not a long-term viable solution for digital libraries. The time has come for digital libraries to look for a better and superior storage technology.
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3.5 Delay in deploying advances Deployment of advanced recording technologies has a direct impact on storage density, cost/bit, and performance of magnetic drives. Hence, this plays a critical role in determining the prospects of magnetic drives in digital libraries. Currently, magnetic discs employ perpendicular magnetic recording (PMR) technology. Unfortunately, PMR has reached its technological limits. A better recording technology, heat assisted magnetic recording (HAMR), was expected to replace PMR a long time ago (Rosenthal et al., 2012). It became apparent that the transition from PMR to HAMR was more difficult and expensive than expected, and hence the deployment of HAMR was delayed. Another technology called bit-patterned media (BPM) (Richter et al., 2006) uses lithographic techniques to create an extremely small location for each bit on the platter. Adoption of BPM is expected to be even more difficult and expensive than the PMR to HAMR transition (Kikitsu, 2009) and, hence,
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costs and, thus, account to around 50 per cent of overall energy consumption of a data centre. With the decline in growth of areal density, more number of drives per repository are needed with each drive having more platters. This results into lesser energy efficient digital libraries, and huge and humongous energy consumption costs.
Figure 5. Inevitability of superparamagnetic limit with increase in areal density
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is expected to face same delays, as shown in Figure 6. All these delays in deploying new recording technologies hinder the growth of areal density and cost drop in magnetic drives, and hence contribute to the growing storage shortage in digital libraries. 3.6 Loss of manufacturing volume of 3.5-inch drives Digital libraries rely on 3.5-inch magnetic drives for long-term preservation of big data. Since they were also used in consumer PCs, they were manufactured in huge volumes. Nowadays, tablets and laptops, which use 2.5-inch drives, have overshadowed PCs and wiped them out from the market. This has led to the loss of manufacturing volume of 3.5-inch drives and, hence, will further slow down the cost drop of 3.5-inch drives. In addition to this, 2.5-inch drives use the same recording technology as 3.5-inch drives, and their cost/bit has been decreasing at a similar rate, but they are typically three to four years later than 3.5-inch drives at reaching a particular cost/bit value (Grochowski and Fontana, 2011; Rosenthal et al., 2012). Thus, at the historic 40 per cent per year price drop, 2.5-inch drives are three to five times as expensive. This implies that digital libraries will suffer from a significant increase in preservation cost/bit, both in case of continued dependence on 3.5-inch drives and migration to 2.5-inch drives (Rosenthal et al., 2012). 4. Competence and adoption of SSDs Flash memory characteristics, such as low latency, low power consumption, small form factor, robustness, etc., make SSDs an ideal storage technology to replace magnetic drives in digital libraries. However, for any storage technology to replace magnetic drives for longterm preservation of big data in digital libraries, the technology has to be competent on many other factors, such as areal density, storage cost/bit, swift adoption, etc. This section evaluates the competence and adoption of the SSD technology to replace magnetic drives in digital libraries. 4.1 Lower capacity and higher cost/bit For any storage technology to replace magnetic drives in digital libraries, the technology must offer huge capacity per drive with low cost/bit. Unfortunately, SSDs offer low capacity/drive and are more expensive than magnetic drives. Many studies have compared areal density, performance, energy consumption, and cost of magnetic drives with that of flash-based SSDs (Narayanan et al., 2009; Albrecht et al., 2013). For a range of data centre workloads, these studies investigated prospects of both completely replacing magnetic drives by SSDs, and using SSDs as a high-performance layer between magnetic drives and the main memory. It was concluded in these studies that due to low capacity/dollar of SSDs, replacing magnetic drives by SSDs is not an optimal solution for data centres, which also CY
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Figure 6. Seagate’s current roadmap for introducing new recording technologies
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4.2 Higher cost of building fabrication facility To suffice all the storage demands of digital libraries by replacing magnetic drives with SSDs, the annual SSD capacity manufactured across the globe has to increase dramatically. An obvious solution is to build many new fabrication facilities for manufacturing SSDs. Unfortunately, it is highly expensive to build such facilities. The cost of making 1 TB of flash is around 49-162 times more expensive than that of making 1 TB of magnetic storage (Rosenthal, 2016), as shown in Figure 7. Another possible solution lies in 3D flash. Three-dimensional flash offers higher capacity per unit than planar flash. Unfortunately, the manufacturing cost of 3D flash is huge as compared to the planar flash. Building them in existing fabrication facilities will certainly result into lower yield and higher cost of 3D flash, besides depletion of planar flash. This shortage of, and higher prices for, flash memory-based SSDs will favour continued dependence on magnetic drives in digital libraries. 4.3 Tax on performance imposed by wear-levelling One main characteristic of flash memory is its high performance. But for some specific workloads, flash performance deteriorates, and tends to be slower than magnetic drives. Studies have confirmed that for certain random write-dominated workloads, the overheads of garbage collector and wear-levelling can sometimes make SSDs slower than magnetic drives (Lee and Moon, 2007). It can be safely argued that although SSDs may be useful as stand-alone secondary storage devices for very high throughput and low-latency applications, they are generally expected to remain in the supporting role to magnetic drives in the foreseeable future in digital libraries (Bhat and Quadri, 2014a, b; Fisher et al., 2012).
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applies to the repositories like digital libraries. In another study, SSDs and magnetic drives were compared on 40 per cent annual growth in bit density (Kryder and Kim, 2009) to confirm their prospects in year 2020. The study concluded that by 2020 all the SSD technologies would be approaching their technological limits. Interestingly, the study also found that magnetic drives will still be one to two decades from the super-paramagnetic limit. This implies that not only will it be very difficult for SSDs to compete with magnetic drives on cost/bit basis, but also SSD technology will find it challenging to offer higher capacity per drive as the technology will approach its technological limits before magnetic storage technology does.
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4.4 Need of disruptive system-level changes To overcome many of their limitations, magnetic drives dictated design of many system software. In particular, applications were restructured to overcome the large gap between sequential and random access latency of magnetic drives (Kasavajhala, 2011). However, SSDs do not have the same large gap in performance between the sequential and random I/O. Therefore, replacing magnetic drives by SSDs can result into overhead added by the earlier performance patch and, hence, deteriorate the performance of SSDs. This necessitates rework at system level, which is disruptive in nature. Furthermore, the distinction between main memory and storage systems needs to be revisited. In context of magnetic drives, this distinction has significance because when a process requests disc access, a context switch is made since I/O completion takes much longer than the context switch. However, with superfast SSDs such a distinction may no longer be valid. Moreover, the overhead of a traditional file system layer is more visible with SSDs, and efforts to make it substantially leaner will be required (Lee et al., 2009; Zhou et al., 2009). 5. Efficiency and overhead of data-reduction techniques One possible approach to deal with storage shortage in digital libraries is to reduce the storage demand. Data-reduction techniques reduce the storage demand by optimising capacity and reducing data footprint. Compression and de-duplication are two popular data-reduction techniques by which long-term preservation of big data can be significantly benefited. However, the efficiency of, and overheads added by, data-reduction techniques need to be analysed first. This section analyses the efficiency and overhead of data-reduction techniques in digital libraries. 5.1 Low data-reduction ratios Data-reduction ratios primarily decide the application of data-reduction techniques in digital libraries. Compression techniques exploit the structure of data to reduce its size. Lossy algorithms promise high compression ratios but do incur loss of information, and hence are not applicable. In contrast, lossless algorithms guarantee the integrity of data during compression/decompression process. This makes lossless algorithms ideal for longterm preservation of big data in digital libraries but unfortunately they suffer from low compression ratios. On the other hand, de-duplication is highly effective in specific scenarios. Reported de-duplication ratios in common business settings range from 1:10 to 1:500 resulting in disc and bandwidth savings of more than 90 per cent (Dutch, 2008). However, the effectiveness of de-duplication depends on multiple factors, such as the type of data, the retention period, the number of users, etc. Hence, the benefits of de-duplication are to be balanced with the availability, usability, maintainability, scalability, and cost characteristics. In general, reduction ratios of data-reduction techniques do not offer any significant gain in reducing the storage shortage in digital libraries. 5.2 High energy-consumption costs Data-reduction techniques are not exonerated from adding energy-consumption costs in digital libraries. Compression reduces data footprint by shifting load from I/O to CPU. This implies that data compression demands extra CPU time for compression and decompression. One study found that for data compression to be energy efficient, multiple parameters play key role (Kothiyal et al., 2009). The study found that for a compression algorithm to be energy efficient, it must consume less compression time. Unfortunately, the study also confirmed that for the files with random content, none of the compression tools they considered provided advantages in energy consumption. This implies that compression techniques do have energy consumption implications.
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De-duplication, such as compression, shifts the load from network and storage devices to CPU. As a result, it is unclear under what circumstances it will lead to energy savings, if any at all (Costa et al., 2011). However, it is certain that there is a trade-off in the de-duplication process between the energy consumed for additional computation and the energy saved by less disc accesses. It has been found that around 10 per cent additional power consumption is demanded by CPU for extra hash computations involved in de-duplication (Zhou et al., 2013). This significantly raises the overall energy consumption cost by 7 per cent. Therefore, there is a strong correlation between the degree of redundancy and energy consumption overhead. This implies that employing data-reduction techniques to overcome storage shortage in digital libraries adds significant energy consumption costs. 5.3 I/O performance degradation Data-reduction techniques may offer some reduction in storage demand of digital libraries, but they also deteriorate the I/O performance of the storage systems. Data-intensive application frameworks, such as Hadoop, apply data compression explicitly at the application level. Unfortunately, this introduces complexity related to seeking in compressed streams (Nicolae, 2011). Also, the framework is not aware of the I/O performed by the storage layer, which limits the choice of optimizations that was otherwise possible, if the schedule of I/O operations was known (Nicolae, 2010). De-duplication also hinders the performance of the I/O system. Applying directly de-duplication to primary storage systems has been reported to cause space contention in memory and data fragmentation on discs (Mao et al., 2016). De-duplication maintains hash index table that adds memory overhead to the existing system. Also, de-duplication adds a layer of indirection that hinders performance. Furthermore, de-duplication tends to de-linearise data placement that significantly affects the performance of magnetic drives (Meyer and Bolosky, 2012). 5.4 High computation overhead Data-reduction techniques also add computational overhead in digital libraries. Compression techniques conserve network bandwidth and improve transfer speed, but the focus is on end-to-end transfers, rather than total aggregated throughput (Nicolae, 2010). This implies that the data are stored in uncompressed form as compression is applied in transit only. Therefore, requests for the same data generate new compression-decompression cycles over and over again. De-duplication provides a trade-off between compute and I/O overheads. Unfortunately, due to huge volume of data stored, de-duplication has a direct overhead in the form of large amount of hash operations and hash index entries. Managing overheads is challenging, and therefore, the performance of de-duplication systems is still being investigated. 5.5 Significant storage overhead Applying de-duplication to reduce the storage demand in digital libraries may actually increase it instead of reducing it. A file recipe (Tolia et al., 2003) is at the core of de-duplication. It contains a list of chunk identifiers each pointing towards data chunks on storage. File recipe is used to reconstruct a file by reading sequentially the chunk identifiers and their associated data chunks. Unfortunately, file recipes can occupy a significant portion of the overall storage in a backup de-duplication system. According to a study (Meister et al., 2013), with an average chunk size of 8 KB, one TB of logical data requires 2.5 gigabytes (GB) of storage to store the corresponding file recipes.
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6. Other storage technologies: obsolete and emerging Besides magnetic drives and SSDs, other potential storage technologies for digital libraries exist. However, they have been either rendered obsolete or are still emerging. Long-term preservation of big data in digital libraries demands high-density storage technologies that consume less energy, result into small capacity footprint, have longer lifetime, etc. Unfortunately, some storage technologies fail to deliver that, while as others promise to offer but are still in their infancy. This section briefly discusses both categories of these technologies. 6.1 Tape storage Tape storage used to be an ideal storage technology for long-term preservation of digital data. However, with the emergence of big data, it has lost its importance. This is evident from the fact that the number of LTO cartridges supplied in year 2015 was around eight times lesser than that of magnetic drives (Rosenthal, 2017). One important factor that has rendered tape in digital libraries obsolete is its storage density. The storage density of tape is far lesser than that of magnetic drives. Furthermore, the working principle for recording information on tape and magnetic drives is basically the same but the advances made in magnetic drives are far ahead of that of tape. Also, as tape advances further, eventually it will face the same technological limits as faced by magnetic drives currently. Therefore, tape is neither currently feasible for digital libraries nor will be in future, and thus has been rendered as an obsolete technology for digital libraries. 6.2 Optical storage Optical storage emerged in 1980s as a promising storage technology to replace magnetic drives. Unfortunately, maximum capacity of optical discs is limited to a few tens of GBs, and hence, they are not feasible for digital libraries. Nevertheless, recent studies based on new photonic principles are disruptive and innovative in their approach, and have demonstrated confinement of light-matter interactions to nanometre scale. This has paved the way towards breaking the diffraction barrier and increasing storage capacity tremendously by using entirely new nanophotonic approaches. Research results show that optical discs with hundreds of petabytes capacity will be available in near future (Gu et al., 2014). They, if adopted, will dramatically alleviate infrastructure, operational, and maintenance costs of digital libraries besides overcoming the storage shortage. Nevertheless, some technical challenges, like lack of rewritable media, need to be overcome before the technology can be adopted by digital libraries. 6.3 Holographic storage The idea of holographic storage was conceived decades ago. Current storage technologies record information only on the surface of the media, which limits their storage density. In contrast, holographic storage is a volumetric storage in which information is stored throughout the volume of the media and not just on its surface. With the emergence of low-cost enabling technologies, significant results from long-standing research efforts, and progress in holographic recording materials and multiplexing techniques, holographic data storage has made significant progress. It is expected that holographic storage will receive more advances in future, and will evolve to suffice all the storage demands and alleviate the financial expenditures of digital libraries (Anderson et al., 2014). However, though holographic storage is very cost-effective technology, it has yet to provide rewritable storage. 6.4 DNA storage DNA was identified as the promising highly dense digital storage technology early in 1960s, and since then DNA as an information storage system has been researched. Studies have demonstrated that DNA has the potential to act as a huge capacity and long-term digital
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storage medium. DNA storage is incredibly dense as it can store one bit per base, and a base is only a few atoms large. DNA storage is so dense that a human body that typically contains 100 trillion cells is able to store approximately 150 ZBs of data in its DNA. It is also extremely stable as DNA can survive for hundreds of thousands of years. Researchers believe that the time is near when DNA storage will fulfil all the storage requirements of long-term preservation of big data (Erlich and Zielinski, 2017). However, certain challenges in DNA storage are to be mitigated to reach this milestone; these include improvements in cost of reading and writing, and throughput. 7. Findings and recommendations 7.1 Findings The study reveals that current storage technologies are not viable for digital libraries for long-term preservation of big data. Adopting these technologies in digital libraries will certainly result into many significant financial implications on digital libraries besides not being able to provide enough storage space to preserve all the big data submitted for its long-term preservation. There are many reasons for this, which include the following. Magnetic drive technology cannot fulfil all the storage demands and alleviate the financial expenditures of digital libraries for the following reasons: •
there is no significant growth in the storage density and cost drop of magnetic drives due to approaching super-paramagnetic limit and recurring delay in deploying new recording technologies;
•
the energy consumption costs incurred by magnetic drives are huge; and
•
the manufacturing volume of 3.5-inch drives has reduced leading to further decline in cost drop of magnetic drives.
SSD technology is incompetent to replace magnetic drives in digital libraries for the following reasons: •
the technology offers lower capacity with higher cost/bit as compared to magnetic drive technology;
•
the technology demands huge investment for building flash fabrication facilities to meet the storage demand of digital libraries; and
•
SSD technology adoption demands system-level disruptive changes, and incur performance problems in presence of big data workloads.
There are also many inefficiencies and overheads related to data-reduction techniques meant to reduce the storage demand in digital libraries. These include low reduction ratios, I/O performance degradation, high energy-consumption costs, and significant computational and storage overhead. 7.2 Recommendations The study recommends that the following: •
policy makers and practitioners should meticulously choose storage technologies for long-term preservation of big data in digital libraries;
•
as a short term solution, research efforts in current storage technologies are required to lessen the impact of storage shortage in digital libraries;
•
as a long-term viable solution, digital libraries should migrate from current storage technologies to emerging storage technologies, such as DNA, holographic, and
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optical, as they hold the potential to overcome the pitfalls of current storage technologies; and •
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academics and industry must give more impetus to research and development of emerging storage technologies for their swift and timely adoption by digital libraries.
8. Conclusion Digital libraries have received significant attention in the recent past for its important role in preserving big data submitted by researchers for long term. Thus, digital libraries are extremely important to share and explore the hidden value of big data. However, sheer volume of big data challenges current storage technologies. This is evident from the widening gap between the data generated and the storage capacity available. Therefore, this study was aimed to find the prospects of current storage technologies for long-term preservation of big data in digital libraries. The study concludes that for digital libraries to preserve the submitted big data for long term and in its entirety (with enough storage for all the data) while ensuring less infrastructure, operational and maintenance cost, current storage technologies is not a viable option. There are many reasons for this. First, dependence of digital libraries on magnetic drives will continue due to incompetence of SSDs to replace them. It is very challenging for SSDs to compete with magnetic drives in providing higher capacity discs and lower storage cost/bit. Besides, their adoption will be disruptive in nature and they are estimated to encounter super-paramagnetic limit before magnetic drives. Second, the growth in areal density of magnetic discs has halted to improve due to approaching superparamagnetic limit and delay in deploying new recording technologies. Manufacturing fixed volume of lower capacity discs results into growing gap between storage supply and demand in digital libraries, besides higher energy consumption costs. Third, as improvements in storage cost/bit of magnetic drives are less, higher storage cost/bit results into higher infrastructure and maintenance costs in digital libraries. Fourth, there exist many inefficiencies and overheads of data-reduction techniques employed for reducing the storage demand in digital libraries. Therefore, current storage technologies can neither fulfil all the storage demands nor alleviate the financial expenditures of digital libraries. Otherwise, potential losses of information due to storage shortage along with significant financial implications are inevitable in digital libraries. The study recommends that policy makers and practitioners should take cognisance of this pitfall of current storage technologies, and should meticulously choose storage technologies for long-term preservation of big data in digital libraries. Also, research efforts in current storage technologies are required to lessen the impact of storage shortage in digital libraries. However, for a long-term viable solution, digital libraries must migrate to emerging storage technologies, such as optical storage, DNA storage, and holographic storage. These emerging storage technologies hold the potential to fulfil all the storage demands and alleviate the financial expenditures of digital libraries. Unfortunately, these technologies have yet to advance further before they can be adopted. Therefore, academics and industry must aggressively pursue research and development in these emerging storage technologies for their swift and timely adoption. References Adu, K.K. and Ngulube, P. (2016), “Preserving the digital heritage of public institutions in Ghana in the wake of electronic government”, Library Hi Tech, Vol. 34 No. 4, pp. 748-763. Adu, K.K., Dube, L. and Adjei, E. (2016), “Digital preservation: the conduit through which open data, electronic government and the right to information are implemented”, Library Hi Tech, Vol. 34 No. 4, pp. 733-747.
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OPPORTUNITIES FOR USING WIKI TECHNOLOGIES IN BUILDING DIGITAL LIBRARY MODELS
Library Hi Tech News Opportunities for using Wiki technologies in building digital library models Elchin Chingiz oglu Mammadov,
Article information: To cite this document: Elchin Chingiz oglu Mammadov, (2014) "Opportunities for using Wiki technologies in building digital library models", Library Hi Tech News, Vol. 31 Issue: 2, pp.5-8, https://doi.org/10.1108/LHTN-02-2014-0009 Permanent link to this document: https://doi.org/10.1108/LHTN-02-2014-0009
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Opportunities for using Wiki technologies in building digital library models Elchin Chingiz oglu Mammadov
We can’t solve problems by using the same kind of thinking we used when we created them (Albert Einstein).
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Introduction Any enterprise that organizes information work and information exchange in today’s environment should: .
.
arrange the information structured to users, based on reliable sources; and organize a knowledge database.
One of the key elements of this system is not only documenting; data editing, and data change but also the need for data review accessibility. “Accessibility” means an unrestricted observation of the process is not dependent on such factors as place and time, intervention within the law, and editing. At present, data access has undergone fundamental changes thanks to technology. Users no longer have to wait for hours for the information they need, thus electronic databases equipped with modern hardware, hypertext with semantic links and effective full-text search engines help to remove the obstacles in this direction ( , 2009). Work principle and structure of Wiki technologies One of the most popular information technologies is using Wikis, which provides a firm platform for corporate collaboration in processing and analysis of any topic. This term was first used by Ward Cunningham, the developer of the first Wiki software, WikiWikiWeb. “Wiki” is a Hawaiian word meaning “fast” or “quick”. Later it was interpreted as “What i know is [. . .]” , 2010). ( In corporate projects, small and medium enterprises use only a few tools
and so problems like duplication, time and licensing frequently arise. Wiki technologies imply group processing of web pages, and at the same time promotes the methodology of this form. Two principal technical functions of this methodology are acknowledged: (1) Simple connection of pages minimizes additional procedures. Online references are much more available than duplication of print pages. (2) Maintaining the history of conducted operations provides the opportunity to meet the requirements of the topics, conduct their analysis, and monitor their urgency. This is the , basic philosophy of Wikis ( 2007). A Wiki does not require a special program, registration on a server or fundamental knowledge of HTML. HTML, with its many cryptic tags, makes the work processes hard to edit, for this reason editing is made in the What You See Is What You Get (WYSIWYG) regime on the base of the programs Adobe Macromedia Dreamweaver or Microsoft FrontPage. New users may learn this very easily; errors can be removed by moving to an older version of pages of current documents. This enables the project manager to keep a record of the achieved results and involve new participants. Wiki technologies arrange the evaluation of knowledge, which aims to involve all users interested in a document on any subject and thereby bring the information contained in that document to a perfect form. Creation of user groups, discussion, arrangement of discussions in the following stages may also serve as advantages of Wiki technologies , 2003). ( As the first step, the project manager (moderator) should submit the general
structure of requirements and agreed upon acceptability (akin to legal) issues. Job distribution of involved persons is identified on the basis of the given structure. In these cases, the sphere of interest of persons included is considered and a quick access to the page of requirements is provided. To intensify the discussion, negative pages or later added pages are provided. In the said case all interested visitors are involved and the analysis, and editing works is intensified. All involved visitors should be informed about the procedures of using a Wiki. These requirements and procedures should be placed on a visible place of the project site, for example, on the home page. One of main problems that arise in information systems based on a Wiki structure is an excess exchange of views, which sometimes leads to negative cases. In this case the person functioning as a moderator must control all discussions, identify their true direction based on the facts. Such a control system helps to identify negative cases in the system, the documents that are required to undergo editing and the intensivity of the interpage transition, etc. (Raman, 2006). Structure of the information placed in the Wiki-system Home page and user pages. As the key elements, the attention is focused on information collection regarding the terms of use and users. In this case, system integration and adaptation proces ses happen in a rapid and collaborative manner. The home page of the project should have an introduction and reflect the information about the work done and the requirements made. Furthermore, to enable new visitors quick access,
Library Hi Tech News Number 2 2014, pp. 5-8, q Emerald Group Publishing Limited, 0741-9058, DOI 10.1108/LHTN-02-2014-0009
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secondary information is provided. User pages reflect this information and the role of each user in the project. Review pages. Such pages help to logically enter the process and understand the given requirements. Reviews make the list of the documents of different types where information may be given about the documents that need editing or already have become ready for use. Thus, we can see that review pages in general, identify the navigation among the pages and discussions between the users. The User story arranges a systematized form of user requirements with special software. In other words, this instrument provides a small description of the operations conducted in the projected system. The other purpose is to respond to the current user requests and meet the requirements in real time. Actor pages identify the role of separate persons with respect to user requirements and use scenario, in other words, each interested person may identify the rights granted to him in the system. User case. The scenario of use identifies the requests and requirements of the users in a structured form. Actually, the structure depends on the requirements of the project. Each scenario contains the history of the operations conducted by the participants. According to the history, all interested persons may investigate whether the use scenario really corresponds to the user’s history (requirements) (FX3, 2007). Global experiences in the types of Wiki technologies and their application As to the latest statistics; at present there exist hundreds of Wiki programs having individual interface and text processing functions. The issue of which of them is more complete, is highly disputable, their compliance from functional, architectural aspects is reviewed. Of course, this difference has its positive and negative aspects. But it should also be noted that these technological opportunities create a collaborative, structured work and is self-justified compared to previous technological opportunities (web-forum, chat, blog, etc.) Wikipedia, Sitizendium,
6
Everything2, Stanford Philosophy Encyclopedia, Letopisi and other virtual encyclopedias can serve as examples. We should note from the above examples that the Confluence system has a commercial nature compared to other systems and is designed for companies and enterprises of any size and has different advantages from a technical point of view. Here is included the system’s localization opportunities, language, support for the control system of “MySQL”, “ORACLE” database on the JAVA platform, integration with “Share Point”, “Adobe Acrobat” and “Microsoft Office” and other forms and possibilities for control (Confluence, n.d.). In this research we focus our attention on noncommercial, open systems. Media Wiki is an excellent example for this purpose. Let us look over this software briefly: MediaWiki Media Wiki is a software mechanism developed by Magnus Manske for creation of web sites with Wiki technologies. The software, known as “UseModeWiki” (as well as Phase1) for the first time after being developed, later it was called MediaWiki. Its first version was developed in PERL language (Practical Extraction and Report Language – practical language for data removal and preparation of reports). Later in 2002, the second Phase was developed in PHP. It should also be noted that this name is often confused with “Wikimedia Inc”, which is the developer and current technical supporter of Wikipedia. This always misleads people who are newly engaged in Wiki technologies (Prasarnphanich and Wagner, 2009). MediaWiki differs from other Wiki systems for the amount of loaded information, users and in general, for its scope. This system creates opportunities such as the copying and printing of materials, individual modules for keeping the system open, regulation of the interface for editing, printing, searching, and navigation. MediaWiki is sometimes called a “Swiss knife” because it offers technical options such as: .
MindMaps – intellectual maps – their main idea is the placement of the topic in the center and a tree form diversification of the related
.
.
information. This technology directs to limitless intellectual thinking more than one-way logical thinking. Latex – this is the automated technical means for collection of texts and preparation of articles, as well as supporting such technical opportunities as numbering of several languages, formulas and sections, availability of crossreferences, placement of images and tables on pages, and preparation of bibliographies. Full-text search – relates to searching on all areas of the text. These areas form a set of characters having logical interrelationships. Such set of characters may contain a title, subtitle, main text, auxiliary information, tables, lists, etc. (FX1, 2009).
Application characteristics of Wiki technologies In applying these technologies sometimes opinions are expressed that the information contained is not complete in meaning and stylistic aspects, does not confrorm to reality and looks like a mass of information. The other opinion is that if the Wiki system is not controlled, it will look like an “information stock” in one year, where no high quality information can be found. In the corporate projects the licenced versions of the information, legal issues regarding its processing are strictly reviewed, each user may carry out editing, correction, notetaking, etc. of the loaded information individually with relevant authorization. Further to the legal issues, diferent issues and ways of solving them arise (Farmer, 2004). For example: Name of pages. In setting references full name of the page or an information about it may be required for the user. Such information can be found in the reviews on existing pages. This technical opportunity is important for ensuring the transition to other pages during the edition of any information. Management of multipage documents. Here the structure of the documents related to one topic that have more pages are controlled, it should be noted that each page has its own corrections archive. For this
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purpose a list of general main references should be concluded and placed in a specific section. Identified pages are marked and noted as a reference to an official page in the system. Furthermore, transitions to official, as well as to older versions exisiting in the archive are possible. Content export is one of other important elements that means the saving of each document in various formats is not dependent on other Wiki systems. Semantic Wiki-opportunities. By applying metadata in creation of documents, it is possible to make the information search qualitative, eliminate the time loss and direct the interpage transition to an intellectual structuring. Availability of Wiki-classifications ensures the systematization of information flows, identification of categories and information search through classification systems and rubricators during user requests. Duplication of content is one of the important factors that has to be removed. Otherwise there will be lots of static and dynamic copies of the same subject and in this case it is needed to remove such copies through special software toolsKolbitsch and Maurer, 2006. The opportunities that Wiki technologies provide to us have been described above. Using these technological innovations in building the digital library models as one of the objects of modern research can create a basis for development of a Wiki digital library and information system which is open to the public. If we consider the structure of Wiki technologies, we can see the opportunities of building absolutely new models of digital library networks. In other words, the distinguishing features of such information systems is that the content is created by project members and by direct intervention of the users within the framework of specified rules. We can see the integration opportunities of Wiki technologies to electronic libraries from the brief explanations given below. Data formation, analytical processing and analysis process. This process identifies formation of metadata and information collections, operationality, and quality processes. Diversity of information resources. Wiki technologies support creation,
Library Hi Tech News Number 2 2014
systematization and openness of the resources in different formats. Wide range opportunities for information search. Here exist different search forms, search options by the names of the pages and news topics, by some text parts on the pages, by different subject indicators, thesauri and dictionaries. Semantics. Referring the information to any field during the information search defines the identification of similar requests, and terms, etc. that are the observance of semantic elements. In other words, “logical search” means relationship of terms with other words, determination of subjects of the requests, formation of the same words with different meanings and identification of use possibilities. Work principles with users. Organization, classification and systematization of work process with users. There are three directions: (1) hardware, struggle forms with vandalism? (2) establishment of administrative procedures and digital library policy; and (3) establishment of commercial relations with users. Technology of collaborative content development. It is one of the main differences of this technology. It reflects creation, presentation, and discussion of the content within the framework of certain rights granted to members. The above opportunities ensure constant dynamics of the web page and updates of resources. Functional opportunities. Formation of different templates, forms, establishment of business processes, organization of online discussions, access to the system through mobile communication and other portable facilities. If we look at the above examples, we can see the technologies already known to us, but using these technological opportunities through the “Wiki way” promises absolutely new forms and directions. Especially considering the environment existing in Azerbaijan, opportunities for experimental development of corporate resources may arise in our republic and become a necessity. Recently, the Institute of Information Technology of Azerbaijan
National Academy of Sciences has done some work in this direction. Azerbaijani-language Wikipedia has been functioning as www.az. Wikipedia.org since 2004. At present, Azerbaijan holds the 53rd position among 271 with 34,000 articles. Wikipedia provides the opportunity to create the information in the Latın and Arabic alphabets. Furthermore, there is activation in controlling the information in the Russian, English, Persian, French and other versions of Wikipedia, which is of great importance from political, historical, and geographical points of view. As said above, significant changes are observed recently, research works have been intensified. The Center for Educational Innovation of the Institute plays a great role in informing advanced students, candidates for a degree and other persons engaged in scientific activity about this global encyclopedia, highlighting the information entering and editing issues, the problems encountered and the global experience and creating initial strong fundamental notions in our society (FX2, 2010).
Conclusions As seen from above, Wiki technologies identify new forms of exchange and collaboration. Surveys demonstrate that Wiki technologies provide both individuality and collaborative technical opportunities, and of course, collaborative work is dominates, and incorporation of information processes and joint activity are supported. As the key elements, free availability of most Wiki software, aggregate formation of content and corresponding documents, management of agreements and other new forms differ from technological opportunities applied in the previously known information systems. Wiki technologies determine the ways of building digital library network models which are structurally different from already known models, as well as new directions in forming information society and solving the problems encountered.
7
ACKNOWLEDGEMENTS
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The author would like to stress his greatest appreciation for his supervisor and Director of the Institute of Information Technologies, ANAS, Dr Rasim Aliguliyev, for supporting him with ideas that pushed him forward to write this article.
pp. 1-3, available at: www.kolbitsch. org/research/papers/2006-CIT-Community_ Building.pdf
REFERENCES
Prasarnphanich, P. and Wagner, C. (2009), “The role of Wiki technology and altruism in collaborative knowledge creation”, Journal of Computer Information Systems, Summer, available at: http://bcdk. Wikispaces.com/file/view/Theþroleþofþ Wiki þ technology þ and þ altruism þ inþ collaborativeþknowledgeþcreation.pdf
http://www. teamlead.ru/display /MAIN/Confluence
Raman, M. (2006), “Wiki technology as a ‘free’ collaborative tool within an organizational setting”, Information Systems and Management, Vol. 23, Fall, pp. 59-66, available at: www.ism-journal. com/ITToday/Wiki.pdf
Farmer, J (2004), “The wide world of Wiki: choosing a Wiki for an element of a fully online undergraduate course”, Incorporated Subversion, June 10, available at: http://radio-weblogs.com/0120501/2004/ 06/10.html Kolbitsch, J. and Maurer, H. (2006), “Community building around encyclopaedic knowledge”, Journal of Computing and Information Technology, Vol. 14 No. 3,
8
ABOUT THE AUTHOR
Elchin Chingiz oglu Mammadov was born in 1983 Baku, Azerbaijan. In 2000-2004, Mammadov received a Bachelor degree in LIS, and in 2006 defended a Masters thesis on “Implementation of new Information Communication Technologies in Azerbaijan libraries” at the Library and Information Department, Baku State University. Mammdov currently works in the ADA University Library as Head of Technical Services and dissertator in the Institute of Information Technologies of ANAS. Elchin Chingiz oglu Mammadov ([email protected]) is based at Library and Information Services, ANAS Institute of Information Technologies, Baku, Azerbaijan.
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This article has been cited by:
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1. Fayez Hussain Alqahtani. 2017. The acceptance of corporate wiki use for knowledge diffusion purposes. Aslib Journal of Information Management 69:6, 642-659. [Crossref]
PERCEPTION OF CLOUD COMPUTING IN DEVELOPING COUNTRIES A CASE STUDY OF INDIAN ACADEMIC LIBRARIES
Library Review Perception of cloud computing in developing countries: A case study of Indian academic libraries Mayank Yuvaraj,
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Perception of cloud computing in developing countries
Perception of cloud computing
A case study of Indian academic libraries Mayank Yuvaraj
33
Central University of Bihar, Patna, India Received 16 February 2015 Revised 7 August 2015 Accepted 29 October 2015
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Abstract Purpose – The purpose of this paper is to explore the perceptions of librarians engaged in Indian academic libraries towards cloud computing. Design/methodology/approach – A structured questionnaire was used to collect responses from the library professionals engaged in Indian academic libraries. Overall, 339 respondents participated in the survey. Descriptive survey method was used in the study. Findings – The findings of the study reveal that library professionals are using cloud-computing tools in their daily works. They want to adopt cloud computing in the libraries to improve library services and avoid redundancy of works. Ubiquitous availability, economy and the various service layers are the core drivers of its adoption in the libraries. The respondents showed their concern over security and data privacy in cloud. Practical implications – The study establishes the fact that the benefits of cloud computing are inadequate to convince the libraries to migrate from the traditional computing paradigm to the cloud. Technological advancement may not transform the cloud into a mainstream technology. To motivate the expansion of cloud computing adoption, emphasis has to be laid upon collaboration between the cloud service providers supplemented by solid cloud legislations which need to be worked out. Originality/value – The paper provides the perceptions of library professionals in response to the adoption of cloud computing. Keywords Cloud computing, University libraries, Cloud computing in India, Indian academic libraries Paper type Research paper
Introduction In recent years, the information technology sector has witnessed technological turmoil in the form of cloud computing. The concept of cloud computing has been defined as “an integrated package of computing services and applications on the web offered as a utility” (Yuvaraj, 2015), where the word “cloud” can be seen as the summation of Internet-based data access and exchange along with low-cost computing and applications. Cloud computing is an outcome of the advancements of various technologies: the Internet, hardware, systems management and distributed computing (Buyya et al., 2009). The concept was in practice earlier, but the term was formally announced by Google in 2010, which most scholars feel was a marketing strategy. A survey conducted by Tata Consultancy Services (TCS, 2012) revealed that the rate of adoption of cloud computing applications is 19 per cent in USA, 12 per cent in Europe, 28 per cent in Asia-Pacific and 39 per cent in Latin America. Scholars have argued that by 2020, most services will be available in the cloud, which shows that the preliminary work for shifting to cloud is in progress. Apart from commercial avenues, medical care,
Library Review Vol. 65 No. 1/2, 2016 pp. 33-51 © Emerald Group Publishing Limited 0024-2535 DOI 10.1108/LR-02-2015-0015
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agriculture, education and libraries in USA and Europe have widely adopted the cloud computing technology, seeing its merits. In India, there has been a wide increase in the adoption rate, with lots of investment in the small and medium enterprises (SME) and data centres. Service provider segments such as TCS, Wipro and Netmagic in India are evolving into major cloud offerings that are playing a major role in cloud computing adoption in India. The environment in which academic libraries are operating has drastically changed in the past few years. With the emergence of the Internet era, libraries are facing problems to justify their services. Cloud computing is an approach to computing that could be worthwhile to academic libraries. Academic libraries can choose how to remain connected in the cloud, to become part of the cloud environment by opting for software, systems and hardware services offered by the cloud service provider. Whatever service they choose, cloud computing can help academic libraries to save time, money and resources, if servers and software were not needed on their premises as they are today. Libraries can focus more directly on services and materials for patrons if their computer hardware and software were handled by IT companies managing the cloud infrastructure. The present paper explores the status of cloud computing practices within Indian academic libraries. Statement of the problem A review of the available literature indicates that cloud-computing platforms are being used in academic libraries for collaborative and resource sharing work. Although cloud computing has been billed as a hot growth and there is a lot of literature on theoretical concepts, few articles deal with the perception of user groups towards the implementation of cloud computing in Indian academic libraries. Before investing in any technology, there is always a need to analyse its problems as well as prospects for the adopting individuals or organisations. Moreover, during the acceptance process of any technology, the perceptions of the user group must be considered. Yuvaraj (2013) tried to explore how librarians in Indian central universities are using cloud computing tools in their daily library services and work, but the study was done during the period when the concept was emerging and gaining popularity. As per the Asia Cloud Computing Association’s Cloud Readiness Index 2014, India recorded the biggest declines in index ranking, falling four positions to 13 in 2014 from 9 in 2012; this suggests insufficient preparedness for cloud computing in many areas (Asia Cloud Computing Association, 2014). However, in 2015, the Indian government has taken several initiatives, such as the Digital India programme, to boost the adoption of cloud computing. The key drivers for the growth of cloud computing solutions in a developing country like India is highlighted in the acceptance of technologies such as the Internet of Things, mobile technologies (3G, 4G) and big data catalysed by the initiatives such as Digital India taken by the government. It is estimated that the Indian government’s Digital India project provides potential opportunities for cloud adoption at a cost of US$19 billion between 2014 and 2018 (CtrlS, 2015). As per the Cloud Computing Innovation Council of India (2014), India has a greenfield opportunity to develop cloud-based solutions that can leverage the latest technology and be optimally suited to the unique requirements of the emerging markets. The council further declares that in India, government departments, industry associations and affiliated bodies have taken a positive approach towards the adoption of cloud computing. In light of these
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developments, a need was identified to conduct a study to examine the problems and prospects of the implementation of cloud computing in Indian academic libraries through the perceptions of librarians in 2015, when several initiatives were being undertaken by the government to improve cloud-computing adoption in India. The investigation of the problems and prospects of the implementation of cloud computing in Indian academic libraries was motivated by three emerging trends. First, among the library and information science (LIS) community, the perceptions of cloud computing are varied, and the literature suggests that there is no universal agreement on how and to what extent cloud computing is related to LIS. If LIS professionals are to be supportive in response to cloud computing technology, it is essential to understand what concepts they possess on cloud computing, how these concepts are interpreted and how their understanding might be developed effectively in response to the cloud-computing phenomenon. Second, there is a strong view in the cloud computing literature that cloud computing is especially beneficial to such organisations, as they have nothing to do with the business of buying, configuring, installing and maintaining servers that are not a part of the day-to-day library mission and services (Breeding, 2011; Corrado and Moulasion, 2011). One must engage in such affairs, unless the work profile demands to do such work. Third, cloud computing is a relatively new entrant to the library environment which requires additional competencies and guidelines for implementation. The biggest problems to be addressed are network connectivity and security (Armbrust et al., 2009; Breeding, 2011; Buyya, 2010; Fox, 2011; McVittie, 2008; Leavitt, 2009). There has been an emerging discussion on the expectations of cloud computing, core competencies required to work in the cloud environment and the problems that affect the adoption of cloud computing in organisations. Objectives of the study The specific objectives of the study were: • to explore the perceptions of cloud computing among academic librarians; • to identify usage level of cloud computing among academic librarians; • to identify the benefits of cloud computing adoption among academic librarians; • to identify the present status of cloud computing in academic libraries; and • to determine the factors critical to the adoption of cloud computing in academic libraries. Research questions Overall, the study attempted to identify the prospects and problems in implementing cloud computing in Indian academic libraries through the following questions: RQ1. What is the perception and attitude of librarians to cloud computing? RQ2. What are the benefits of cloud computing for libraries? RQ3. What are the competencies needed in adopting cloud computing in libraries? RQ4. What is the present status of adoption of cloud computing in libraries? RQ5. What factors are critical for the implementation of cloud computing in libraries?
Perception of cloud computing 35
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Methodology For the present study, a descriptive survey research method was used. To collect data, a questionnaire, and sometimes direct interviews, was used. For ranking the overall perceptions of respondents, a scoring system designed by Sarrafzadeh (2005) was used with some modifications, as represented in Table I below: The study carried out by Sarrafzadeh was related to the study of perceptions, to examine the implications of knowledge management in the library and information professions. As the present study was targeted towards a similar theme – to study the perceptions – but a different research problem – i.e. cloud computing – the scoring system was adapted. Five-point Likert scales were used for the ranking of the perceptions. The population for the study involved 450 library professionals (university librarian, deputy librarian, assistant librarian, information scientists, professional assistants, semi-professional assistants, technical assistants and library assistants) who were engaged in computer operations, collected from a pilot study. Data were collected from a sample of 339 library professionals. For all the responses, descriptive statistics including mean, percentile, etc., were calculated. The data were summarised by means of descriptive, associational and comparative statistics. Data analysis Most of the data were summarised by means of descriptive, associational and comparative statistics. Tables, pie charts and histograms were used to present the data visually in terms of the perceptions and opinions of the respondents. Libraries under study The details of the libraries selected in the study are presented in Table II. Before analysis, it would be appropriate to look into the basic facts of the libraries. Library collections Library collections are the mainstay of all library services. The total collections of the libraries under study have been presented in detail in Table II. As can be observed from the table, amidst the selected central university libraries, Delhi University Library System has a colossal collection of 1,400,000, followed by Aligarh Muslim University with 1,200,000. Banaras Hindu University ranks third with a collection of 1,050,000. Indian Maritime University, Tamil Nadu, and Indira Gandhi National Tribal University, Madhya Pradesh, have the least collections of 8,000 and 9,000, respectively. When asked about the interest of library professionals towards the adoption of cloud computing in these libraries, about 95 per cent of respondents showed a high level of interest. It shows that, irrespective of big or small collections, libraries are interested in cloud computing. Respondents’ demographic information Analysis unveils that 65 per cent of the respondents included information scientists, professional assistants, semi-professional assistants, technical assistants and library assistants, while 20 per cent were assistant librarians. Of the remainder, 9 per cent were deputy librarians and 6 per cent were university librarians. The results also show that in Indian academic libraries, 77.63 per cent are male library professionals and 19.87 per cent are female.
Mean 1 to 1.44 Mean 1.45 to 2.44 Mean 2.45 to 3.44 Mean 3.45 to 4.44 Mean 4.45 to 5
Mean range
Strongly disagree Disagree Don’t know Agree Strongly agree
Not used Partially used Moderately used Used Extensively used
Not important Minor important Moderately important Important Very important
Rating scale
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Not implemented Little important Moderately implemented Implemented Extensively important
Perception of cloud computing 37
Table I. Scoring system of the study
Note:
17 18 19 20 21 22 23 24 25 26 27 28 29
14 15 16
9 10 11 12 13
a
Year represents the year in which the library became fully functional
Aligarh Muslim University (AMU), Uttar Pradesh Allahabad University (ALU), Uttar Pradesh Assam University (AU), Silchar Babasaheb Bhimrao Ambedkar University (BBAU), Uttar Pradesh Banaras Hindu University (BHU), Uttar Pradesh Central Agricultural University (CAU), Manipur Dr Hari Singh Gour University (HSGOU), Madhya Pradesh English & Foreign Languages University (CIEFL), Andhra Pradesh Guru Ghasidas University (GGU), Chhattisgarh Hemwati Nandan Bahuguna Garhwal University (HNBGU) Indian Maritime University (ITM), Tamil Nadu Indira Gandhi National Open University (IGNOU), New Delhi Indira Gandhi National Tribal University (IGNTU), Madhya Pradesh Jamia Millia Islamia (JMI), New Delhi Jawaharlal Nehru University (JNU), New Delhi Mahatma Gandhi Antarrashtriya Hindi Vishwavidyalaya (MGAHV), Maharashtra Maulana Azad National Urdu University (MANU), Hyderabad Manipur University (MAU), Manipur Mizoram University (MIU), Mizoram Nagaland University (NU), Nagaland North Eastern Hill University (NEHU), Meghalaya Pondicherry University (PU), Pondicherry Rajiv Gandhi University (RGU), Arunachal Pradesh Sikkim University (SU), Sikkim Tezpur University (TU), Tezpur Tripura University (TIU), Tripura University of Delhi (UOD), Delhi University of Hyderabad (UOH), Andhra Pradesh Visva-Bharati University (VBU)
1 2 3 4 5 6 7 8
Table II. Background information of academic libraries
Name of university
Central Library Manipur University Library Central Library Central Library Central Library Ananda Rangapillai Library Central Library Teesta-Indus Library Central Library Central Library Delhi University Library System Indira Gandhi Memorial Library Central Library
Zakir Husain Library Central Library Mahapandit Rahul Sanskritayan Central Library
Central Library Central Library University Library Library Documentation Division Central Library
Maulana Azad Library Central Library Rabindra Library Central Library Sayaji Rao Gaekwad Library Central Library JawaharLal Nehru Library Ramesh Mohan Library
Name of the library
1998 1980 2001 1992 1973 1985 1984 2007 1994 1987 1922 1974 1951
1920 1968 1976
1983 1973 2008 1985 2008
1920 1887 1994 1996 1916 1993 1920 1973
1998 1980 2001 1994 1973 1986 1984 2008 1994 1987 1922 1975 1952
1973 1976 1978
1984 1973 2008 1986 2008
1960 1916 1994 1998 1917 1993 1920 1988
Year of establishment University Librarya
38
Sr. no.
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32,498 160,000 87,431 32,916 238,682 216,707 208,400 30,685 59,602 42,806 1,475,729 1,388,066 376,521
373,000 560,000 15,000
150,000 145,458 8,000 130,000 9,000
1,186,139 715,141 25,00 13,098 1,061,378 29,000 300,000 160,914
Total collection
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Attitude of librarians towards cloud computing As a means to apprehend the attitude of librarians towards cloud computing, respondents were asked to show their level of concurrence on certain statements about cloud computing. The responses have been presented in Table III. The majority of respondents (66 per cent) agreed that there was nothing new in cloud computing. Cloud computing, for them, was largely a platform where the services were being delivered through the Internet. To a larger extent, it was a form of Internet-based computing. However, about 20 per cent of the respondents dissented from the statement and considered it to be a new term which has emanated from pre-existing technologies, while 12 per cent remained undecided. Moreover, respondents stood disunited over whether cloud computing was a marketing strategy, a new technology, a new revolution or a new philosophy. The issue has been subjected to huge contestation of interpretations in LIS literature. Most scholars are of the opinion that cloud computing was largely marketing hype created by cloud computing giants such as Google, Amazon and Microsoft. Most of the respondents (68 per cent) regarded cloud computing as something that they were already doing by another name and were unclear about the term. The finding validates the studies of Cohn et al. (2002), Hoy (2012) and Romero (2012), who discovered that cloud computing was already in use in libraries in the form of Gmail, Google Docs, bibliographic management and integrated library systems. Furthermore, respondents unanimously agreed that cloud computing enables librarians to focus on their daily mission and services rather than being involved in information technology operations. LIS literature shows that with the involvement of librarians into IT operations such as installation, configuration and updating, the day-to-day mission of libraries has been compromised (Breeding, 2011; Corrado and Moulasion, 2011).
Perception of cloud computing 39
Willingness of librarians to adopt cloud computing The majority of respondents (86.97 per cent) showed high levels of consent with regard to the adoption of cloud computing solutions. Figure 1 shows the various reasons for the higher interest towards adoption of cloud computing in libraries. Analysis reveals that “Less indulgence in library IT activities”, “Ubiquitous availability”, “Pay per use”, “Unlimited storage capacity” and “Greener library services” are the core issues that attract the respondents and, driven by these issues, library
Statements Cloud computing is a form of Internet-based computing Cloud computing is either a marketing strategy or a new technology, or a new revolution or a new philosophy Cloud computing was in practise in the libraries much before the term became pre-eminent Cloud computing enables librarians to focus on their daily mission and services rather than being involved in IT operations Cloud computing in particular is highly beneficial to the libraries
Mean
Overall mean
3.50
Agree
3.05
Undecided
4.05
Agree
4.60
Strongly agree
3.50
Agree
Table III. Librarians’ attitude towards cloud computing
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No capital investment 8% Ubiquitious availability 11%
Reduced technology obsolence 6% Pay-per-use 10%
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Greener library services 9%
Faster deployment and development 5%
Figure 1. Reasons for adoption of cloud computing in libraries
High Computing Power High Scalability Less indulgence in library IT activities Diverse support Greener library services Pay-per-use No capital investment
Location and Device independency 7% High Scalability 5% Less maintenance Less 5% indulgence in library IT activities Unlimited storage 16% capacity Diverse support 9% 3% High Computing Power 6%
Location and Device independency Less maintenance Unlimited storage capacity Faster deployment and development Ubiquitious availability Reduced technology obsolence
professionals’ keenness about its adoption. Figure 2 presents the various stages of cloud computing in libraries. It is evident from the figure above that cloud computing is still in its preliminary stage, and the respondents are discussing the issues that revolve around the same. Although a few cases of use and pilot studies came to light, no library professional claimed to fully utilise cloud-computing-based solutions. Familiarity with cloud computing technology Cloud computing service layers When asked about the familiarity of library professionals with various service layers, respondents showed higher variations in their opinions, as represented in Figure 3. This shows that approximately 48 per cent of respondents were aware of free software as a service and 28 per cent were well acquainted with application as a service. In all, 12 per cent of respondents were familiar with paid subscription as a service, while 9 per cent were aware of platform as a service and 8 per cent of infrastructure as a service. Cloud computing deployment models Figure 4 shows the level of familiarity of the librarians with the various cloud computing deployment layers. It shows that approximately 44 per cent of the respondents were familiar with “Private cloud”, 26 per cent with “Community cloud” and 15 per cent with “Public cloud”.
30%
Perception of cloud computing
In discussion, 22%
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Level of Implementation
25% 20% 15%
41
10% Case study, 2% Pilot study, 1%
5%
In implementation, 3% In use, 2% Do not want, 5%
0% -5%
0
45% 40% 35% 30% 25% 20% 15% 10% 5% 0%
1
2
3
4
5
6
7
Percentage of respondents
Figure 2. Level of cloud computing adoption
42% 28%
12%
9%
8%
5%
3%
3%
Figure 3. Familiarity with service layers
Familiarity with cloud-based tools A closer investigation of the results presented in Table IV shows that cloud-based productivity tools, cloud-based information collection tools, cloud-based social networking tools, cloud-based tools for social groups and cloud-based email and communication tools were heavily used. Remaining indicators were in the range of 3.22 to 2.85, which shows that they were moderately used by the respondents. Benefits of cloud computing for academic libraries To find out the perceptions of the librarians over the potential benefits associated with cloud computing, respondents were asked to rate their level of agreement over the benefits of cloud computing for academic libraries. The responses have been presented in Table V.
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Public cloud
Private cloud
Hybrid cloud
Others
Community cloud
4% 11%
42
15%
26%
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Figure 4. Familiarity with deployment models
44%
Cloud-based tools
Table IV. Level of the use of cloud computing tools and practices
Cloud-based email and communication tools Cloud-based tools of social group Cloud-based social networking tools Cloud-based information collection tools Cloud-based event management tools Cloud-based file sharing services Cloud-based video and presentation tools Cloud-based storage/back-up tools Cloud-based operating systems Cloud-based productivity tools Cloud-based library solutions
Mean
Overall mean
4.58 4.45 4.38 4.32 2.25 3.22 3.00 2.85 3.15 3.75 1.53
Statement Implementation of cloud computing will eradicate the requirement of libraries to own infrastructure facilities Cloud computing provides a large number of processing power as well as unlimited storage capacity to the libraries Adoption of cloud computing will provides the opportunity of ubiquitous computing to the library users Adoption of cloud computing can minimise the capital expenditure Table V. and check the wastage of library resources as the payment is based Perceptions of on the utilisation of services librarians towards the potential benefits Cloud computing is easy to deploy that offers the latest functionality and supports diverse platforms of cloud computing
Used Used Used Used Partially used Moderately used Moderately used Moderately used Moderately used Used Partially used
Mean
Overall mean
4.50
Strongly agree
4.57
Strongly agree
4.25
Agree
4.40
Strongly agree
4.10
Agree
The respondents showed a strong position on the statements “Implementation of cloud computing will eradicate the requirement of libraries to own infrastructure facilities”, “Cloud computing provides a large number of processing power as well as unlimited storage capacity to the libraries” and “Adoption of cloud computing can minimize the capital expenditure and check the wastage of library resources as the payment is based
on the utilization of services” with mean scores of 4.50, 4.57 and 4.40, respectively. Furthermore, respondents agreed over the statements “Adoption of cloud computing will provides the opportunity of ubiquitous computing to the library users” and “Cloud computing is easy to deploy that offers the latest functionality and supports diverse platforms” with a mean score of 4.25 and 4.10, respectively. It is indicative of the fact that librarians foresee a new elixir for the library arena through cloud computing.
Perception of cloud computing
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43 Adoption of cloud computing technology in libraries It was essential to determine the extent to which the academic librarians were interested in adopting cloud computing. To find out that whether the mean of the respondents extended to the medium degree of agree, a t-test was performed. The results of the t-test are presented in Table VI. The analysis reveals that, in general, approximately 79.49 per cent of the librarians agreed with the idea of the adoption of cloud computing within libraries. It can be concluded that, due to the inherent benefits of cloud computing, library professionals show eagerness for its adoption. Support and integration of library services with cloud computing To determine the possibilities of support and integration offered on cloud computing platform to library services, a t-test was used to find whether the mean of the respondents attained the medium degree of agreement. The results have been presented in Table VII.
No. 1
Statement
Cloud computing is an attractive economic option for the academic libraries 2 Academic libraries constantly focus on new IT tools and services to increase the quality of library services 3 Cloud computing will help the librarians to focus on library mission and services rather than being involved in IT 4 All the academic libraries have high-speed internet lines and uninterrupted services that will serve as a base for cloud computing 5 Cloud computing will ensure ubiquitous availability of library services that will increase the satisfaction of the users 6 Library services in the cloud environment will facilitate an environment for elearning initiatives carried out by the academic institutions All statement of the field
Mean
Proportional mean
Test value
p-value
Rank
3.52
73.72
7.31
0.000*
5
3.22
72.91
7.04
0.000*
6
4.93
88.96
9.68
0.000*
1
3.86
79.52
9.27
0.000*
3
4.37
86.67
9.41
0.000*
2
3.74
75.21
8.53
0.000*
4
3.94
79.49
10.26
0.000*
Table VI. Means and test values for the adoption of cloud computing in libraries
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Table VII. Means and test values for support and integration of library services with cloud computing
Statement
Universities continuously update their systems, technological services and applications to keep pace with the technological development that will provide strong support for adoption of cloud computing 2 Adoption of cloud computing technology will help to activate new library services 3 Adoption of cloud computing in libraries will improve the quality of the library services 4 IT services and applications provided in the libraries can be easily integrated with that of cloud computing 5 The service providers of cloud computing services offer it free of cost which is independent of devices making it a viable solution 6 Data and the services in cloud computing can be accessed from anywhere All statement of the field
Mean
Proportional mean
Test value
p-value
Rank
3.28
63.72
3.11
0.000*
6
5.22
82.91
7.04
0.000*
3
4.93
78.96
6.88
0.000*
4
4.86
77.52
6.67
0.000*
5
6.37
88.67
7.91
0.000*
1
6.24
87.21
7.53
0.000*
2
5.15
79.83
7.52
0.000*
The analysis reveals the fact that approximately 79.83 per cent of respondents contended that support and integration was offered on a cloud computing platform for library services. Skills of library staff in cloud computing environment This section deals with the skills of the library professionals engaged in IT operations. The results have been presented in Table VIII, determined through t-test. The analysis reveals that approximately 77.33 per cent of respondents agreed that with basic IT skills, they could work in the cloud computing environment. Security concerns of cloud computing Security is a core concern for migrating to cloud computing solutions. This section was provided with various statements drawn from the available literature to find out the perception of the respondents, determined through t-test. The results are presented in Table IX. The analysis reveals the fact that approximately 82.16 per cent agreed that security is the biggest impediment to the adoption of cloud computing in academic libraries. Economic solution through the adoption of cloud computing The section examines the pre-established notion that cloud computing will effectively reduce costs, determined through t-test. The results are shown in Table X.
No.
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1
Statement
Basic knowledge of IT is enough to look forward to adopt cloud computing 2 Cloud computing technology helps in the development of the spirit of creativity and innovation 3 With the adoption of cloud computing technology, library staffs get rid of updating, configuring and installing software 4 Academic libraries continually look for technological developments to meet with the expectations of the users 5 Technological innovations motivate the staff to serve the interest of the parent institution in an improved manner 6 Library staffs need training especially in the construction, development and deployment of the cloud-based library services All statement of the field
Mean
Proportional mean
Test value
p-value
Rank
2.28
59.72
⫺0.31
0.370
6
4.82
77.91
7.04
0.000*
3
Perception of cloud computing 45
4.23
74.96
6.88
0.000*
4
4.06
72.52
6.67
0.000*
5
6.37
88.67
7.91
0.000*
1
6.24
87.21
7.53
0.000*
2
4.66
77.33
6.21
0.000*
The analysis revealed that approximately 78.04 per cent of respondents agreed with the statement that cost reduction can be achieved through the adoption of cloud computing. Impediments to the adoption of cloud computing in academic libraries: perception of librarians Table XI indicates the ranked order of the obstacles expressed by the respondents. The highest ranked problems of the implementation of cloud computing in academic libraries as ranked by the respondents were security, reliability, lack of standards, connection dependence and loss of IT control and ownership. Summary of findings Cloud computing has brought about a revolution, replacing traditional IT practices for organisations striving to cut down computing costs as well as to execute painless IT. Cloud computing is considered to be an engine of innovation and has the potential to grow into an unavoidable phenomenon for academic libraries due to its immense benefits. Being at a nascent stage, cloud computing has been subjected to scholarly argument regarding viewpoints on its status, as well as relevance within libraries. According to the findings, there was a huge variation in the level of awareness and use of the cloud computing phenomenon among LIS professionals. Analysis revealed that LIS professionals were already working in the cloud computing environment by another name, which was therefore only a change in nomenclature. Further, they recognised cloud computing as a useful concept for academic libraries on various accounts and showed a high level of familiarity with the concept. LIS professionals in
Table VIII. Means and test values for skills of library staff in cloud computing environment
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No. 1
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46
Table IX. Means and test values for security concerns in cloud computing
Statement
Security of data stored in the cloud is the biggest impediment in the adoption of cloud computing 2 Transparency in data ownership, management and location should be maintained to work in a cloud computing environment 3 Safety and reliability of the data in the cloud demands for a proper agreement between the university library and the service provider 4 Cloud computing services provided by Google, Microsoft and others are safer than the old system 5 The services and applications of cloud computing provided by the service provider companies are difficult to hack and do piracy 6 The cloud solution hosted by libraries on their own is safer for putting the library data, reports, etc All statement of the field
Mean
Proportional mean
Test value
p-value
Rank
4.38
87.72
15.31
0.000*
2
3.76
75.91
6.47
0.000*
6
3.93
78.96
6.88
0.000*
4
3.86
77.52
6.67
0.000*
5
4.92
88.67
17.91
0.000*
1
4.74
84.21
7.53
0.000*
3
4.26
82.16
11.67
0.000*
Indian academic libraries have a positive attitude towards the adoption of cloud computing in their libraries. They were already using cloud-based productivity tools, cloud-based information collection tools, cloud-based social networking tools, cloud-based tools for social groups and cloud-based email and communication tools. No formal initiative or cloud computing policy for the libraries has been taken up by the librarians to adopt cloud computing in academic libraries. However, librarians in this study acknowledged the fact that most of them used cloud-based tools over traditional software to accomplish their work. On identifying the reasons for using cloud computing, it was found that reduction of cost, ease of use and painless IT operations were the major reasons for migrating to the cloud environment. LIS professionals foresee that cloud computing will offer new opportunities and avenues to serve library users efficiently and effectively. They expect that cloud computing will help libraries to improve their library services and make them more significant to their universities by means of effective library operations and user services, transforming academic libraries into learning organisations, reducing the chances of data redundancy and ensuring ubiquitous availability of the library services. Despite the fact that cloud computing offers enormous benefits, the findings reveal that the use of cloud services raises new issues with regard to privacy, security, trust, lock-in with service providers and data transfer capacity. Before planning to implement cloud-based solutions, there is a need to review aspects of privacy legislation. Trust
No.
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1
Statement
The parent academic institution viz. universities always focus on modern IT systems that aim to reduce costs 2 Shifting the operations and library services into the cloud will reduce costs 3 Cloud computing services are less expensive than the old systems 4 Cloud services are offered free of cost that helps to communicate with other librarians, save and share data 5 Cloud computing reduces the expenses on purchasing hardware, servers, software or maintenance 6 Cloud computing solutions are beneficial to the libraries due to its ability to control the cost by use 7 Cloud computing platform can be used to provide innovative services without increasing the cost 8 The adoption of cloud computing technology converts the capital expenditure in the IT operations of the libraries to ongoing expenses All statement of the field
Mean
Proportional mean
Test value
p-value
Rank
3.17
73.72
6.81
0.000*
7
3.22
74.91
7.02
0.000*
6
3.83
78.96
8.78
0.000*
4
3.07
72.67
6.54
0.000*
8
4.13
82.67
11.07
0.000*
2
4.03
81.21
9.53
0.000*
3
4.28
83.72
13.31
0.000*
1
3.76
76.62
7.13
0.000*
5
3.68
78.04
15.62
0.000*
agreements between service providers and the users of cloud services need to be carefully drafted. There is a need to overcome the fear of loss of data. Implications of the research findings The findings of the study have a number of theoretical and practical implications. To implement cloud computing effectively in academic libraries, LIS professionals need to clarify the concept of cloud computing. The implementation of cloud computing will not succeed if LIS professionals merely view it as online applications or consider it to be something that they were already doing by another name – as found in this study. There is a need to clarify the concept, the underlying technologies and the tentative domain of application in LIS services. There is a clear understanding among the LIS professionals that cloud services represent both an opportunity for a massive rollout of computing services and also potential for the development of LIS services. Cloud-based productivity tools, cloud-based information collection tools, cloud-based social networking tools, cloud-based tools for social groups and cloud-based email and communication tools are the most popular cloud computing solutions used by LIS professionals. These platforms and tools can be brought to the notice of other LIS professionals, and they can be encouraged to adopt these solutions in their libraries.
Perception of cloud computing 47
Table X. Means and test values for economic solution through the adoption of cloud computing
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Problems
Security Reliability Regulatory compliance Complexity Privacy 48 Connection dependence Service provider dependence Technology dependence Data management Skills Integration Lock-in (switching costs) Loss of IT control and ownership Cost uncertainty Lack of awareness Lack of suppliers with satisfactory credentials Lack of standards Lack of liability of providers Table XI. Librarians’ perceived Internet congestion problems of Over-subscription of services implementing cloud Unclear scheme in Pay-per-use approach computing Data centre location
Rank
(%)
1 2 8 9 10 4 16 20 15 21 11 19 5 12 13 18 3 7 17 14 22 6
96.0 92.0 76.8 74.2 73.8 84.0 68.4 60.4 69.7 59.8 72.5 62.8 81.0 71.7 70.8 64.8 89.0 77.6 66.2 70.2 53.5 78.0
Moreover, cloud services and technologies also present challenges for privacy and security of data, an area that is receiving considerable public attention and also policy scrutiny. To adopt cloud computing solutions in an academic library setup, it is therefore advisable to draft a formal cloud library policy, as well as guidelines in which the concept, applications, and other concerns may be outlined. It is surprising that international library bodies such as international federation of library associations and institutions (IFLA), american library association (ALA) or special libraries association (SLA) have not yet shown concern over this issue. Suggestions for future research This study investigated the perceptions only of academic librarians, to identify the problems and prospects of implementing cloud computing in academic libraries. It is important to bear in mind that academic libraries are part of the university system, and cloud computing cannot function in isolation. Therefore, there is a need to investigate cloud computing from the viewpoint of different stakeholders; the cloud computing environment in conjunction with libraries involves not only librarians but also users and faculty members. Given the projections of cloud service providers over the next 2-3 years, as the cloud matures, researchers may evaluate cloud candidates playing an active role. One of the limitations of the present study is that it covers only academic libraries, particularly central university libraries. To generalise the impact of findings, it should be replicated in other types of libraries, gaining an insight and a thorough perspective of cloud-based libraries. Another useful area of research may be the investigation of cloud computing practices in organisations other than the library sector. As the review of the
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literature suggests, cloud computing is being used in learning, health centres and agrarian practices. In the current study, no attempt was made to compare perceptions of cloud computing practices in various sectors. A comparative study of cloud computing in different sectors, as well as different cloud computing platforms would therefore prove to be useful. It would monitor the different sectors and avenues of cloud computing implementation and may help librarians to make informed choices in terms of the implementation of cloud computing. Conclusion and recommendations The results of the present research indicate that librarians are using cloud computing applications in academic libraries. However, librarians are also worried about privacy, security and legal jurisdiction in the cloud. Academic libraries work under larger structures with different objectives and missions to support their parent organisations. As an academic library is a unit in an organisation (university or institute), initiating cloud-based library services at its own level is a daunting task. Universities can support academic libraries by providing adequate finance to develop cloud-based services, but there are various issues on which higher authorities and administrators ought to take initiative. Besides the cost, academic libraries ought to think about the quality of cloud services and simply the viability of cloud computing for them. Some of the central issues for implementing cloud computing in academic libraries that need to be worked out in the future are: • frame a “Canon of Cloud Libraries” that should be the guiding principle for the alliance of libraries with cloud computing; • frame cloud library strategic planning and guidelines; • addressing the problem of cloud library legislation; • addressing the expense and justifying the issues over cloud library budget; • defining the scope and boundaries of library services in the cloud; • resolving the data trust, privacy, migration and backups; and • identifying competencies for the new breed of cloud librarians. References Armbrust, M., Fox, A., Griffith, R., Joseph, A.D., Katz, R., Konwinski, A., Lee, F., Patterson, D., Rabkin, A., Stoica, I. and Zaharia, M. (2009), “Above the clouds: a Berkeley view of cloud computing”, available at: www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-28.pdf (accessed 5 February 2015). Asia Cloud Computing Association (2014), “Asia cloud computing association‘s cloud readiness index”, available at: www.asiacloudcomputing.org/images/research/ACCA_CRI2014_For Web.pdf (accessed 18 July 2015). Breeding, M. (2011), “Automation marketplace 2011: the new frontier”, available at: http://lj. libraryjournal.com/2011/03/library-services/automation-marketplace-2011-the-newfrontier/#_ (accessed 23 November 2015). Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J. and Brandic, I. (2009), “Cloud computing and emerging IT platforms: vision, hype, and reality for delivering computing as the 5th utility”, Future Generation Computer Systems, Vol. 25 No. 6, pp. 599-616, available at: http://dx.doi. org/10.1016/j.future.2008.12.001 (accessed 23 November 2015).
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Buyya, R. (2010), “Cloud computing – the next revolution in information technology”, in Internet Computing 2010 proceedings of the international conference in Nevada, USA, 2010, IEEE, pp. 2-3. Cloud Computing Innovation Council of India (2014), “Cloud computing innovation in India: a framework and roadmap. White Paper 2.0”, available at: www.ccici.in/Whitepaper_2_0.pdf (accessed 18 July 2015). Cohn, J.M., Kelsey, A.L., Fiels, K.M. and Salter, D. (2002), Planning for Integrated Systems and Technologies: A How-To-Do-It-Manual for Librarians, Facet, London. Corrado, E.M. and Moulasion, H.L. (2011), “Perspectives on cloud computing in libraries”, in Corrado, E.M. and Moulasion, H.L (Eds), Getting Started with Cloud Computing, Facet, London, pp. 9-13. CtrlS (2015), “The state of cloud adoption in India”, available at: www.ctrls.in/blog/cloudadoption-in-india/ (accessed 18 July 2015). Fox, A. (2011), “Cloud computing – what’s in it for me as a scientist?”, Science, Vol. 331 No. 6016, pp. 406-407, available at: http://dx.doi.org/10.1126/science.1198981 (accessed 23 November 2015). Hoy, M.B. (2012), “Cloud computing basics for librarians”, Medical Reference Services Quarterly, Vol. 31 No. 1, pp. 84-91, available at: http://dx.doi.org/10.1080/02763869.2012.641853 (accessed 23 November 2015). Leavitt, N. (2009), “Is cloud computing really ready for prime time?”, Computer, Vol. 42 No. 1, pp. 15-20. McVittie, L. (2008), “Cloud computing: it’s the destination, not the journey that is important”, available at: http://devcentral.f5.com/weblogs/macvittie/archive/2008/11/03/cloudcomputing-its-the-destination-not-the-journey-that-is.aspx (accessed 2 March 2015). Romero, N.L. (2012), “‘Cloud computing’ in library automation: benefits and drawbacks”, The Bottom Line: Managing Library Finances, Vol. 25 No. 3, pp. 110-114, available at: http://dx. doi.org/10.1108/08880451211276566 (accessed 23 November 2015). Sarrafzadeh, M. (2005), “The implications of knowledge management for the library and information professions”, actKM Online Journal of Knowledge Management, Vol. 2 No. 1, available at: www.actkm.org/userfiles/File/actKMjnl/2005/The%20implications%20of%2 0knowledge%20management%20for%20the%20library%20and%20information%20 professions%281%29.pdf (accessed 15 March 2015). TCS (2012), “The state of cloud application adoption in large enterprises: a TCS global trend study – March 2012”, available at: www.mitcio.com/sites/default/files/sponsorwp/TCS_Cloud_ Study_Report_0312-1.pdf (accessed 5 March 2015). Yuvaraj, M. (2013), “Cloud computing applications in Indian central university libraries: a study of librarians’ use”, Library Philosophy and Practice (e-journal), available at: http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article⫽2397&context⫽libphilprac (accessed 20 July 2015). Yuvaraj, M. (2015), “Cloud computing software and solutions for libraries: a comparative study”, Journal of Electronic Resources in Medical Libraries, Vol. 12 No. 1, pp. 25-41, available at: http://dx.doi.org/10.1080/15424065.2014.103479 (accessed 23 November 2015). Further reading Christensen, C.M. (1997), The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, Harvard Business School Press, Boston, MA.
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McDermott, C. and O’Connor, C.C. (2002), “Managing radical innovation: an overview of emergent strategy issues”, Journal of Product Innovation Management, Vol. 19 No. 6, pp. 424-438, available at: http://dx.doi.org/10.1111/1540-5885.1960424 (accessed 23 November 2015). McLoughlin, I. and Harris, M. (1997), Innovation, Organizational Change and Technology, Thompson Business Press, London. Oldham, G.R. and Cumming, S.A. (1996), “Employee creativity: personal and contextual factors at work”, Academy of Management Journal, Vol. 39 No. 3, pp. 607-634, available at: http:// links.jstor.org/sici?sici⫽0001-4273%28199606%2939%3A3%3C607%3AECPACF%3 E2.0.CO%3B2-F (accessed 23 November 2015). Quinn, J.B. (2000), “Outsourcing innovation: the new engine of growth”, Sloan Management Review, pp. 13-28, available at: http://sloanreview.mit.edu/article/outsourcing-innovationthe-new-engine-of-growth/ (accessed 23 November 2015). Corresponding author Mayank Yuvaraj can be contacted at: [email protected]
For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected]
Perception of cloud computing 51
This article has been cited by:
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1. Ali Tarhini, Khamis Al-Gharbi, Ali Al-Badi, Yousuf Salim AlHinai. 2018. An Analysis of the Factors Affecting the Adoption of Cloud Computing in Higher Educational Institutions. International Journal of Cloud Applications and Computing 8:4, 49-71. [Crossref] 2. Mohammed A. Al-Sharafi, Ruzaini Abdullah Arshah, Emad A. Abu-Shanab. Factors affecting the continuous use of cloud computing services from expert's perspective 986-991. [Crossref] 3. YuvarajMayank, Mayank Yuvaraj. 2016. Ascertaining the factors that influence the acceptance and purposeful use of cloud computing in medical libraries in India. New Library World 117:9/10, 644-658. [Abstract] [Full Text] [PDF]
PROBLEMS AND CHANGES IN DIGITAL LIBRARIES IN THE AGE OF BIG DATA FROM THE PERSPECTIVE OF USER SERVICES
The Journal of Academic Librarianship 45 (2019) 22–30
Contents lists available at ScienceDirect
The Journal of Academic Librarianship journal homepage: www.elsevier.com/locate/jacalib
Problems and Changes in Digital Libraries in the Age of Big Data From the Perspective of User Services
T
⁎
Shuqing Lia, , Fusen Jiaoa, Yong Zhanga, Xia Xub a b
Department of Information Management and Information System, College of Information Engineering, Nanjing University of Finance & Economics, Nanjing 210023, China Department of Business Administration, School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Big Data Digital library User service Service innovation
Based on the investigation of the position of user service for constructing digital libraries in the big data era, this paper points out that not only data resources of modern digital library have the characteristics of big data, but also the existing library services need to use big data methods to achieve reform and innovation, including resource transferring, resource utilization, social identity, thinking innovation. We focus on the importance of user services and types of big data resources that digital libraries can utilize, which include big data within libraries such as user behavior data and digital literature resource, and other big data outside libraries such as scholarly big data. We also examine the problems and potential of digital libraries in the age of big data relative to data, technology, services, and users. Using existing big data resources and considering the characteristics of current users' needs from the perspective of users, more effective ideas and methods to improve existing services in digital library can be put forward. At the same time, it is the personalized need of users in the age of big data that constitute the driving factor for the development of digital library from resource-sharing service to useroriented service.
Introduction Libraries are important social institutions that help people access various information resources. With the continuous development of information technologies, libraries have been evolving constantly, and this has greatly expanded library services and improved their efficiency and effectiveness. American scholars proposed the concept of a digital library in the early 1990s. After years of practical application and development, digital libraries have gradually become an important aspect in the development of modern libraries. The development of digital libraries is directly related to the development of information technologies, particularly Internet technologies. A digital library is an innovative library service that uses information technology, and nearly every major development stage of a digital library is accompanied by major technological changes. From the perspective of information-based civilization, information technology has enabled libraries to go through the traditional stages of automation and develop digital libraries. It is expected that innovations will continue as libraries respond to technological developments, such as mobile Internet services and artificial intelligence (Wang, 2017). From this perspective, big data will inevitably have a profound impact on the services provided by modern digital libraries.
⁎
With the promulgation of the Chinese State Council's Guiding Opinion on Actively Promoting the Internet + Action in July 2015 and the Chinese National 13th Five-year Plan in March 2016, emerging information technologies, such as big data and artificial intelligence, are attracting increasing attention. For example, the 2017 Chinese Government Work Report refers to artificial intelligence (Li, 2017). Correspondingly, in the digital library field, the application of new technologies is gradually receiving increasing attention. The IFLA (The International Federation of Library Associations and Institutions) Trend Report ranks artificial intelligence as one of four major technology trends and argues that artificial intelligence has three major implications relative to the future of libraries, i.e., next-generation browsers that extend beyond keyword searches and semantic analysis of web content, integrated speech recognition and machine translation to support real-time multilingual translation, and multiple and complex translation and identification of cloud services for complex multivariate web content (IFLA, 2017). The 2017 New Horizon Report Library Edition included artificial intelligence as one of six important technological developments for the library community (NMC, 2017). Therefore, we must reexamine the impact that digital information technologies, such as the Internet, big data, and artificial intelligence, have had on the development of digital libraries. Based on this analysis,
Corresponding author. E-mail address: [email protected] (S. Li).
https://doi.org/10.1016/j.acalib.2018.11.012 Received 11 July 2018; Received in revised form 20 November 2018; Accepted 26 November 2018 Available online 05 December 2018 0099-1333/ © 2018 Elsevier Inc. All rights reserved.
The Journal of Academic Librarianship 45 (2019) 22–30
S. Li et al.
we propose a direction and route planning for the next phase of digital library development. This article focuses on the development of digital libraries in big data environments and the important role that personalized information services play.
therefore, we consider an important question: Do digital libraries possess and need big data? Some studies have investigated basic research questions relative to the application of big data in digital libraries, such as “How,” “When,” and “If” questions (CNI, 2015). The basic nature of these questions reflects the confusion that many people have about whether big data exist in digital libraries. Big data do not have a fixed threshold size, and different disciplines and applications may have different definitions. Moreover, various data reduction techniques have been developed. For example, to reduce storage requirements and retain important information, OCR can identify text in an image. Most current information systems are incapable of storing, processing, or analyzing big data. Even if such systems are updated routinely, they may not be able to cope with increasing amounts of data. For example, due to the growing number of Internet users and web sites, the number of visitors and scope of their use are also growing, and this growth trend appears to be accelerating (Chen et al., 2015). Some data may still be relatively small compared to big data. For example, many digital libraries initially store data collections submitted by many individual researchers. Although the total amount of these data is not large, it demonstrates the same overall rapid growth trend as big data and can exceed the capabilities of existing library systems (Salo, 2010). In addition, with the continuous growth of multimedia resources, such as images and videos, the data types in a digital library have become increasingly diverse. The widespread use of linked data on the web has also significantly increased the amount of data that digital libraries must handle (Brandon, 2013). In addition, the data connections between academic literature in digital libraries are receiving increasing attention. In addition, the number of connections between different datasets is increasing. Such connections provide more opportunities for coauthor analysis and co-citation analysis, etc. These new data connections enrich the existing data possessed by digital libraries (Teets & Goldner, 2013). Of course, big data provide an effective decision-making function, and effective use of such resources results in a smaller inputoutput ratio in a library. In fact, some libraries have begun to use this big data decision support function to perform input-output analyses (Fister, 2015). Big data analysis also provides libraries with more effective ways to fully use such data. The value of big data primarily relates to resource purchase decisions, personalized reader services, hot spot analysis, and the creation of shared academic environments. Some studies have suggested that big data can have a huge impact on data mining, data management, data visualization analysis, auxiliary decision-making mechanisms, and reader behavior analysis (Wu, Su, & Deng, 2013). Given focused scientific analysis, it has become easier for libraries to utilize big data to implement knowledge services (Zhang, 2016). Therefore, as the main gathering place of human knowledge, libraries are gradually storing increasing amounts of big data. Not only has the size and type of data reached the big data standard, but integration and comprehensive utilization of external big data have become key to improving service levels in existing libraries. Therefore, digital libraries are expected to play an increasingly important role in big data analysis and information utilization. However, compared to other domains, research into big data relative to libraries is limited. For example, the overall research competitiveness is generally weak, research efforts are scattered, and there is a lack of empirical research. In particular, there is insufficient emphasis
Thinking about the relationship between digital library and big data Further understanding of big data The literature indicates that the big data concept was first conceptualized in 2001 by Laney in his research notes. Laney believes that an important feature of big data is that it cannot be processed effectively by traditional data management tools (Laney, 2001). Currently, ways to combine and integrate big data to improve business designs and service management are being considered. In the following, we consider “big” and “data” separately. Big is easy to understand. Data resources and types have increased significantly. Most big data-processing methods emphasize high-speed and efficient use of large amounts of data. To extract value from big data, effective and efficient data-processing technologies are required. Since 2010, with the maturity and widespread application of cloud computing and artificial intelligence, big data has rapidly shifted from theoretical research to technical application. Typically, human knowledge acquisition from data involves a standard process shown in Fig. 1. The conceptual level of data is relatively low because it refers to original data resources extracted from the objective world without processing or analysis. Information, which forms the basis for people's thinking and decision-making, is the product of effective data processing. Data analysis results can be conceptualized as a higher-level knowledge system. Obviously, the formulation of big data seems like a big step back in terms of data. However, the concept of big data reflects a conscious emphasis on the data. The huge amount of data currently available is unprecedented and has led to new research conditions and opportunities. As mentioned previously, traditional data are often limited to a single stage of interaction within a data-processing life cycle. Once a single life cycle ends, the data lose value. With the emergence of big data, new data associations are being identified, and new data-related business requirements are emerging. With big data, data association can lead to the discovery of new knowledge (Qian, Cheng, Liang, & Wang, 2015). Cross-sectoral, cross-business, customized, and personalized needs continue to emerge. The value that can be obtained continues to attract attention. The potential and effectiveness of data-based decisionmaking is strengthened continuously, and the data dividend is gradually realized (Li, Lv, & Li, 2016). Do digital libraries have big data? Demand for big data is growing in the digital library field (De Mauro, Greco, & Grimaldi, 2016). However, relatively few studies have investigated digital libraries relative to big data. One reason for the lack of such studies is that many people think traditional database management systems can handle the daily data storage and business processing requirements of digital libraries (Xu, Du, Wang, & Liu, 2017). For example, the National Geological Library of the People's Republic of China only stores approximately 710,000 data items, which is considerably less than common commercial big data systems. Some scholars believe that distributed systems are unnecessary (Hessman, 2013);
Fig. 1. Standard knowledge acquisition process.
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industries, talk to data experts to ensure that data produce value, and enhance competitiveness and improve the effectiveness of management decisions. Data literacy can be considered a new manifestation of information literacy in the age of big data. It is obvious that advances in technology lead to changes in thinking that ultimately lead to shifts in business-related change management (Huang & Li, 2016). Big data thinking also promotes data management, analysis, use, and services in digital libraries. These significant changes will help librarians identify data processing and data service requirements and can also help librarians understand emerging responsibilities. Big data environments require a change in library management thinking, i.e., from resource management to data management (Gao, 2015). In other words, based on the acquisition and analysis of big data, big data thinking involves the potential relationships among data and library services. This type of thinking has four primary characteristics: regularity, unbiasedness, relevance, and openness (Tian, 2015). Clearly, in relation to digital libraries, big data thinking will facilitate management and service innovation (W.W. Zeng, 2014).
on the practical application of big data in libraries (Lu, 2014). There are many reasons for this. For example, some scholars believe that digital libraries tend to be self-sufficient relative to organizational management and often do not consider emerging technologies (ProQuest, 2013). Some scholars argue that the reasons may be associated with budget constraints (Horstmann & Witt, 2017). Even though hardware costs are declining rapidly and software usability is increasing, it remains difficult to determine whether such changes will help alleviate the difficulties associated with insufficient funds for digital library construction (Wang, Xu, Chen, & Chen, 2016). Some scholars think that information security and privacy protection issues are unavoidable when considering big data applications. For example, private information may leak if user information is used to identify user interests. Such risks will also have an impact on the extensive application of big data technology in digital libraries (Lee, 2013). Even the advantages of traditional data resources have produced some disadvantages relative to large-scale research into and the application of big data in the field of libraries. For easy use, such data must be transformed, e.g., digitization of paper documents, and integrated with other data. However, these data have experienced various development stages and various information processing technologies have been adopted. Therefore, unlike emerging fields, transforming and integrating traditional data resources often incurs significant processing and conversion costs, which represents another difficulty for digital libraries relative to the application of new information technologies.
Big data in digital libraries Importance of user services The big data era has resulted in new challenges for digital library services and opportunities for transformation. Scientific research is shifting to data-intensive research, and data science has become increasingly important in scientific research. As the service center for documentary information and data resources, libraries face internal and external pressure to change. Libraries must adapt to user requirements and improve content delivery, ease of use, and service responsiveness (Coelho, 2011). From a service resource perspective, traditional digital libraries often only function as a provider of data resources and play a leading role in knowledge dissemination. However, they do not pay sufficient attention to individual user interests and data content requirements. With the rapid development of big data technologies, the library community has experienced a profound shift from the traditional massservice model to a personalized service model. The service mode and content of digital libraries have gradually shifted from literature-based to user-based, and from general to personalized services. This shift enables library users to acquire knowledge more effectively and facilitates further optimization of information resources (Li, 2012; L.X. Wang, 2015). The ability to address personalized user knowledge requirements has become more urgent. For example, users are now more interested in autonomously tagging information content, reorganizing knowledge, sharing it widely, and enhancing interactions with other users through online reference tools. There are three reasons why the big data era has resulted in a need for personalized services in digital libraries.
Impact of big data on digital libraries The emergence of big data exacerbates the problem of information overload that has been associated with the development of the web. The costs and difficulties associated with efficient use of information resources are increasing. For example, from a user perspective, big data can produce a growing requirement for weak information among users (Carole, 2008), which is often characterized by fuzzy structures, unclear knowledge scope, a lack of clear and systematic retrieval and discovery steps, and the need to dynamically explore a great number of documents to achieve only partial satisfaction. However, in the face of complex and dynamic research issues, the requirement for weak information is becoming increasingly important and common. In terms of technological talent in the big data era, there is growing need for data specialists, such as data engineers with big data-processing knowledge and skills, data analysts who can model big data and perform application analyses, and data stewards who can manage and discover valuable data and ensure data availability. Data stewards include data archivists, subject librarians, and other similar professionals (Boulton, 2014). Librarians have helped researchers collect and analyze scientific data for a long time. In the age of big data, subject librarians are becoming more specialized data librarians. Skill-related knowledge for data librarians includes open data licensing agreements, intellectual property rights, rights management, data management plans, resource utilization, data analysis and application, big data deployment and related architectures, management of institutional repositories, data reference, and data publication (Journal Center of the School of Information Management, Wuhan University, 2016). In addition to changing user information requirements, the impact of big data on user thinking may be also apparent. In fact, thinking determines action. Big data not only provide the means and the possibility of action, big data also provide a completely new way of looking at the world based on the acquisition of as many facts and data as possible to make judgments. The more data we acquire, the more likely we are to eliminate the uncertainty of information, and knowledge with greater value can be created. Therefore, we must consider data content, data found in business, and how such data are used. In many cases, we have some data but do not know how to use the data. Therefore, big data thinking can be considered a type of data literacy. In other words, it is a conscious willingness to understand data significance in all
1) The continuous production of huge amounts of data makes obtaining effective information more difficult. The information overload problem is becoming increasingly prominent relative to limited user information acceptability and time costs. Therefore, finding content that users are genuinely interested in from large-scale data resources and filtering irrelevant information to reduce unnecessary information screening costs has become key to improving user satisfaction in digital libraries. 2) The ever-increasing amount of data leads to ever-increasing data connections. Such connections can not only improve our understanding of data and facilitate ways to find target data more effectively and efficiently, but also provide the necessary and basic conditions for further exploration and analysis of hidden values which traditional single-data resources cannot provide. In large amounts of data, there are a great number of associations among the data, such as the associations among user social data, associations 24
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between users and users, associations among users and resources, and associations among different resources. Such connections allow users to obtain the required service content more easily and quickly. In addition, such connections can generate new user information requirements and can be used to create new types of information services by combining existing user interest patterns. 3) Users obtain and analyze data to obtain knowledge related to a particular application. The understanding and application of the knowledge content is determined by the data and also depends on the specific application environment and current information requirements. Links, interactions, and integration of semantic and application relationships will have a significant impact on user understanding of the obtained data (Zhang, 2005). We often refer to such external information as a context. Personalized information services consider combinations of both contextual information and the user service content.
browsing, retrieval, and downloading histories), user interaction information, and other logs. By analyzing these user data and combining them with user social information on the web, we can perform more comprehensive and accurate analysis of library user reading habits, resource utilization behavior, and network usage. These data represent valuable resource wealth; however, unlike other resources, the value of data resources is often reflected in the process of sharing and promoting data. For example, in 2012, the Harvard University Library publicly published big data about more than 12 million books provided by 73 library branches. Each collection offers up to 100 different values for each attribute, including data, manuscripts, maps, videos, and audio. Users can access the data through the U.S. Digital Public Library (Audrey, 2012). Through long-term relationships with many different types of libraries, in April 2012, OverDrive (Ohio, USA) claimed in its first Big Data Report that they can provide an amount of user usage data collected from these libraries and collaborative e-book sites, such as Buy It Now. These data include the circulation of e-books, reader book browsing and download history, library sites' daily traffic logs, and other information. They freely provide these data to publishers and other libraries that have a working relationship with their library. In addition, using data analysis and mining technologies, they found that the circulation of e-books has significant influence on publishing houses and book dealers, i.e., publishers can determine which books to publish according to readers' electronic book browsing and download histories, and distributors can use this information to automatically determine acquisition bids, build reader-recommended bibliographies from a user perspective, and organize marketing activities from a publisher perspective (Spolanka, 2016). With the continuous development of library technology and data resources, modern digital libraries can more easily collect various usage data than before, including the usage of various databases and user feedback, such as information on social media. Libraries have developed many useful evaluation tools and have integrated quantitative and qualitative statistical data from library surveys and user usage into existing data resources (Ichiko, 2010). With the advent of big data, an increasing number of libraries prefer to use sophisticated tools, such as learning analytics for data analysis and research performance analysis, compared to traditional data analysis methods, such as statistical methods (Cox & Jantti, 2012). We should also pay attention to different voices. For digital library services, are there limitations and problems with the combination of big data? This type of big data analysis method can be understood as a typical data-driven research methodology. The characteristics of the data will play a very direct role in related research. Some scholars argue that the introduction of external data sources for digital library analysis carries with it some potential risks. For example, there are some shortcomings in big data information relative to social media, such as privatization, amateurization, and balkanization. In addition, the use of library user logs is an effective and typical big data analysis method; however, due to a lack of user motivation, information requirements, and specific information about their true meaning, simply using such logs often leads to oversimplification and misjudgments for related research, which can be also be considered a common manifestation of amateurization (Niu, Zhang, & Chen, 2014). In addition, limited by the present technical capabilities of the digital library, simply pursuing the acquisition and combination of big data does not often have a direct impact on process transformation and service improvement. Therefore, the digital library must adapt to the actual requirements of big data processing as quickly as possible and even introduce related evaluation tools to measure the effect of using big data resources. For example, the University of Washington Library launched the “Making the Numbers Speak” project to visualize key data resources using visualization tools and later introduced the Balanced Scorecard Strategic Management Framework to identify useful big data for their own library development between 2010 and 2011 (Qin, 2014).
By providing personalized services, digital libraries can greatly enhance the diversity of user service and provide users with relevant information resources, which reduces user time costs and the costs associated with organizing library information (Gu, 2010). The design of a fully functional and user customizable digital library service system involves cognitive and behavioral design processes. In addition, employing an effective, user-centric human-computer interface can generate a sense of user independence, which is a key factor of user satisfaction (Ferran, Mor, & Minguillón, 2005). Libraries are an important component of public social service systems. The advent of big data has driven changes to the traditional service modes of digital libraries; however, it has also resulted in unprecedented challenges. To satisfy increasing user demand for personalized service, digital libraries must keep up with technological developments and enhance the application of big data technologies to optimize system construction. Although digital libraries were relatively slow to research both big data and individualized applications of big data, related research and applications have attracted significant attention. Although libraries are faced with transformation requirements, we believe that many of the difficulties faced by existing traditional data resource services in libraries can be resolved using big data technologies and personalized services. Big data resource in digital libraries Big data in digital libraries Not only do digital libraries need to explore how to use large external data resources, their internal data are increasingly showing the characteristics of big data (Li & Zhang, 2013). Understanding a digital library's internal big data resources is a prerequisite of effective use of the resources. In a digital library, big data primarily comprise electronic and document resources, user information data, such borrowing information and browsing history, and various formalized data that are gradually increasing in library information services (X.Y. Wang, 2015). For example, the National Library of China has the largest digital document repository and service base in China with more than 1000 terabytes of digital resources and a growth rate of 100 terabytes per year (NDLC, 2018). At the end of 2011, the National Library held 561.3 TB of digital resources. In 2007, it held only 200 TB (DLPP, 2018), which represents a five-fold increase in in a decade. This amount of data is significant. It represents a great deal of knowledge that can reveal relationships among various knowledge themes, entity objects, and carrier forms, as well as research elements, scientific literature, science and technology projects, event activities, experts and scholars, product technologies, organizational structures, and presentations (J.X. Zeng, 2014). Digital libraries also have a wealth of user data resources, such as user registration information, user behavior information (such as 25
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Big data outside digital libraries In the face of big data resources outside digital libraries, the more realistic and feasible choice for a digital library is using a third-party service for effective data processing and analysis, such as digital resource database providers and Internet platforms. Such service providers can offer very professional and comprehensive big data resource services based on advanced technological development capabilities with the accumulation of existing data resources. This digital library resource is often referred to as scholarly big data (Huang, Lu, Cheng, & Gui, 2016). For example, in November 2017, Baidu Scholar (launched June 2014) announced on its official website that it had collected 1.29 million worldwide academic sites, indexed 1.2 billion worldwide academic sites, built 400 million academic documents, created four million homepages of Chinese scholars, and collected 10,000 Chinese academic periodicals with three million keywords. In November 2017, Baidu Library claimed to provide 190 million web documents, nearly 700,000 video course resources, and over one million academic journal articles. Microsoft Academic reported 50 million records in 2010 with annual average increases of 2.7% to 13.6% for different databases (Larsen & Ins, 2010). At the same time, many research literature resources can be obtained free of charge on the web. Some scholars have stated that the proportion of free scientific literature accounted for 43% between 2008 and 2011 (Eric, Didier, & Philippe, 2013). Many well-known IT companies involved in the information retrieval of academic web resources provide users with convenient and efficient services, such as Google Scholar, PubMed, ArXiv, and CiteSeer. Academic big data have many different characteristics from general big data. For example, academic big data are often highly correlated such that there are many intrinsic links of great analytical value, such as citations, co-occurrence of authors, and co-occurrence of author institutions and publishers, which have always been an advantage in library and information sciences. In addition, ambiguity is obvious in the data due to the diversity of subject terms and professional expressions. For example, the widespread use of acronyms often brings out challenges related to synonyms and polysemy. Same author's names or same affiliations and other external literature information (especially in English) are often written in a variety of different format. In addition, only recently has the diversity of citation formats been resolved. What is more complicated is that copyright protection and intellectual property rights also limit the dissemination and usage of relevant literature resources (Williams, Wu, Choudhury, Khabsa, & Giles, 2014).
similar subjects in the humanities (92.53% and 84.64%, respectively) (Hao & Lu, 2017). This indicates that the knowledge of these disciplines is relatively enclosed and has less impact on related disciplines. Such related academic information is often restricted to dissemination within limited channels. As a result, progress in the field of libraries has been relatively slow in terms of acceptance and assimilation of external professional knowledge and technology. For the digital library, a large number of rapidly changing information technologies exacerbates this problem. Therefore, even in the digital library stage, libraries are accustomed to adopting substance-oriented and relatively isolated digital strategies, which is another reason library development tends to be conservative (Wu, 2017). It is obvious that the present library service model can be considered an extension of the traditional library model. Libraries primarily focus on traditional literature rather than information content; thus, the operating model of libraries remains highly dependent on the traditional academic exchange system based on commercial publishing. Most digital libraries tend to focus on digitization of documents, the organization and storage of digital documents, document retrieval, and document delivery. It is undeniable that this traditional model has value; however, overreliance on or restriction of such resources and services puts the future of the library at risk (Zhang, 2011). Several problems that should be explained separately are outlined in the following. 1) Resource delivery: The digital library has changed. In the past, the transfer of resources was performed by librarians. In a modern context, the bulk of this work is performed by the users themselves. Borrowing paper documents can be fully automated. Accessing digital resources has become a basic learning skill, and most users have mastered the skills and technology required to discover and access various information resources. In fact, the status of libraries as a former information service center is being marginalized. OCLC's Library Awareness 2010 showed that very few people used library portals to find information (the 2005 survey also showed only 1%) (Gauder, 2011). This dilemma faced by the library precisely reflects the need for library service reform in the age of big data. 2) Resource utilization: Digital libraries can provide the functions and forms of digital resource services that a traditional library cannot. However, with the continuous development of academic big data on the web, current user intent and methods to use the digital library's resources are also being weakened constantly, and users prefer to give priority access to other digital resources on the web. The importance of social discovery is increasing; however, the importance of libraries relative to providing credible resources is diminishing. Many scholars have found that an increasing number of users employ web search engines to obtain information. In contrast, users of library academic resources have shown a declining trend (Chua & Goh, 2010). For example, CNKI, Google Scholar, and other tools are often used to acquire literature, and web encyclopedias are used to interpret the concept of knowledge. Relative to library lending services, i.e., the traditional core service, e-book service platforms, such as Google Books, have become more convenient for users. In addition, mobile reading and other mobile applications provide users with more choice. 3) Social recognition: In a social context, the value of libraries and librarians is decreasing in the age of big data. For example, the subject librarian, as a former assistant researcher that engages literature discovery and innovation analysis, is no longer the mainstream of library services due to the continuous development and changes in user needs. The Ithaka Institute of the United States performed faculty surveys over three years and found that user identification of the library as an information portal gradually declined over the survey period (Long & Schonfeld, 2010). However, the library as a storage or preservation institution remains essentially unchanged, and the library as a “purchaser” is gradually increasing. Moreover,
Innovation in digital library service modes in the big data age Problems Throughout history, libraries have been constantly innovating and revolutionizing themselves to adapt to changes in human society and technology (Zhang, 2001), and, in the big data era, the existing functions of digital libraries are facing new changes. Some problems have reached a point that demands the “theory of extinction of the library” proposed by American librarian Lancaster must be considered (Lancaster, 1982). With the widespread use of Internet technology and mobile devices, the library is no longer the only information service organization. The library's relatively conservative management system gradually weakens its own core competitiveness; therefore, rapid development of information technology once again leads to wide-scale reintroduction and discussion of the “theory of extinction of the library” (Luo & Yao, 2014). From the perspective of historical development, libraries have always been archival organizations with books as the primary resource, and the degree of association with society has been relatively small, which has made the development of library science and related disciplines face similar situations. For example, library, intelligence, and archival sciences have higher rates of self-citation and citation by 26
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Some scholars suggested that the work of librarians should be considered an extension that complements the work of data scientists in the age of big data. For example, the knowledge and experience of librarians relative to understanding user needs can provide data scientists with a better basis for information services. In addition, librarians' archive management and data curation skills can also provide data scientists with the possibility of exploring the long-term value of data resources. Even in the midst of an overlap with data scientists, librarians can provide advantages relative to their ability to control data, data structures, and data conversion (Stanton, 2012). The American Library Association considers that a library's basic mission is to provide unrestricted access to information. In other words, with the help of professional librarians, libraries use existing and emerging science and technology to collect, organize, store, archive, and save information to provide a variety of information access routes and services (Huang & Hu, 2012). Obviously, the big data era expounds new characteristics of this mission. Modern libraries, especially digital libraries, should learn to use various methods to collect, organize, store, file, and save big data to realize more effective and convenient information services. We have sufficient reason to believe that the big data era provides new opportunities relative to library development.
the function of teaching and research support, which is considered important by library directors, has not been universally accepted by faculties. Therefore, many scholars have pessimistic views about the development of libraries in the context of emerging technologies (Li, 2002). 4) Change in thinking: Relative to libraries, the traditional concept of identifying knowledge as a collection of resources, identifying user need as access to literature, and identifying the provided service as the retrieval and acquisition of resources has been completely divorced from practical modern requirements. However, unlike technological changes with very short iterative cycles, changes in thought are both more difficult and longer lasting. The resulting misunderstanding and habitual inertia can often only be identified and changed after problems occur. After investigating a number of research librarians, OCLC believes that the value of libraries, library-related human resources, and library technology will face a crisis that could have a huge impact (Michalko, Malpas, & Arcolio, 2010). Possibility of change Asking questions and facing difficulties does lead to improvement. We should further change our thinking and clearly identify the direction of the next move. For example, in the 1993–2000 strategic plan released by the British Library, the “Library Collaboration” chapter was replaced by a chapter that discusses “Leadership, Partnerships and Collaboration” and highlights cooperation (Donlon, 1993). In August 2013, IFLA's Declaration on Library and Social Development emphasized that libraries should realize their own value in the participation of social development and further emphasize cooperation with social development rather than focusing on only libraries and reading (Wu, 2017). All of these will better demonstrate the social functions and occupational values of libraries. In fact, through observing the overall history of library development, many scholars believe that the library has always been able to make new acquaintances and survive through constant “evolution” by adapting to change. Changes in libraries will never stop because the need for information and knowledge will never stop (Fang, 2013). For example, Changping concluded that public libraries and the national economy are in a synergistic development relationship based on the anatomy of the related elements of public libraries and association analysis of the national economy (Hu & Luo, 2005). This view has a long history. When the 62nd IFLA General Assembly was held in Beijing in 1996, relevant Chinese scholars put forward a similar view. For example, Jianzhong proposed that information service is the core value of a library. Libraries should actively meet the challenge of informatization and constantly consolidate their status as information centers (Wu & Koenig, 1996). Jingzheng also thought that libraries should adapt to the trend of informatization to realize their value and serve society with multifunctional, networked, multi-carrier, and intelligent modes relative to the rise of the knowledge economy and the development of network information (Li & Ma, 1999). In 2004, the annual meeting of the Library Society of China reshaped the spirit of the Centenary Library. In 2007, the core value of the library was an important issue that aroused heated discussion, where the transformation of the library's spirit relative new technological conditions was emphasized. The library gradually realized that service and value are its most precious features rather than technology. Some scholars argued that there is an imbalance between tools and values in the study of library science. It is believed that the integration of tools and values should be the focus of library science development in the 21st century, and the study of library value should be the focus of future research (Xiao, 2004). For example, for public libraries, the “public” essence is the maximum value, and the function of library services includes archive preservation, promotion of the economy, and the improvement of cognition (Liu & Wen, 2012).
Changes to digital libraries relative to user services Libraries have begun to use big data thinking to examine their resources and services, and research into the effective integration of big data by digital libraries is emerging. For example, in 2009, Yongcheng and Huilan of Donghua University Library first proposed the concept of data security and the big data in library circulation (Luo & Chen, 2009). Some scholars have proposed that the construction of a digital library requires a big data environment relative to changes in data environments, changes in scientific research methods, changes in user information literacy, and information technology development (Chen, Qian, & Dai, 2014). From a technical perspective, others scholars have proposed the use of advanced technologies, such as Service Oriented Architectures and cloud computing, to realize a new library service system with all-media resource management capabilities, business management capabilities, and resource-discovery capabilities within the entire domain (Yin & Liu, 2013). However, scholars have different opinions about what specific aspects of change to include. Such scholars generally believe that there will be great diversity in the utilization of big data and in the reform of existing library service modes. Some scholars propose that library reform under a big data environment should proceed relative to three aspects, i.e., resource construction, technology application, and library service. For resource construction, it is necessary to expand the scope of resources, increase the breadth of resource integration, and increase the depth of resource processing. In terms of technology application, semantic technology should be emphasized, the application of clustering technology should be strengthened, data analysis technology should be widely used, and retrieval technology should be improved. In terms of services, digital library services should be enriched by shifting services from a common passive model to a more proactive, automatic, and personalized model (Su, 2015). Some scholars think that changes in five specific aspects are particularly obvious: (1) changes in the international data environment require digital libraries to manage big data; (2) changes to scientific research methods require digital libraries to support data-driven research environments; (3) the transfer of the innovation model requires digital libraries to meet the needs of business development; (4) changes in user information literacy require digital libraries to meet the needs of knowledge search; and (5) digital libraries must adapt to development and changes in information technology to upgrade service platforms (Chen et al., 2014). According to OCLC's Information Context framework, service revolution in digital libraries in the big data era can be summarized from three main aspects: the basic information environment, the behavior of 27
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Fig. 2. Methods and trends of digital library transformation in the big data era.
information users, and the basic operation mechanism of the information service (OCLC, 2007). According to this idea, we show an overall framework in Fig. 2. We can describe the overall function of a digital library as a process of “data–technology–service–user”. This organization is also close to the four core components of the internal construction of digital libraries, i.e., resource construction, platform construction, new media services, and standards construction (Han, 2016). Each step of this process in a big data environment has its own improvement direction and change method.
devices. 4) The user is the object of digital library services. However, the goal of a digital library service is to satisfy the user's information needs; thus, it is more important to consider current user requirements from the user's perspective to more effectively propose ideas and methods to improve existing services. In addition, individual user needs drive the development of digital library services from resource-sharing to user-oriented services (Wu, 2009). For example, for general library users, existing studies have shown that the information literacy of library users has undergone great changes with the ongoing popularization of information technology. Scientific researchers served by subject librarians have data resource and dataprocessing capabilities that librarians do not. Therefore, the role of “helping users” in a traditional library service should be shifted to “prompting users” and “suggesting to users.” However, McKinsey predicted that nearly one-half of data scientist jobs in the United States will be vacant in 2018 (Manyika et al., 2011) because training data scientists incurs great costs. In fact, this situation is the same in the library field because, for librarians to adapt to big data-processing requirements, they must acquire complex expertise in related fields, such as statistics, computer science, and information science. However, short-term rapid training cannot satisfy such requirements (De Mauro et al., 2016).
1) Data in traditional digital libraries primarily include literature data, digital resource collections, database resources, and other forms. Therefore, the construction of digital library resources based on big data should emphasize two goals. The first is to use big data to improve the storage and utilization of existing data resources, integrate big data resource into existing digital library resource systems, and enrich the existing data size and type. The second is to integrate newly generated data in new data formats and associated data on the web with the existing data resources of digital libraries. Such data resources provide the possibility of improving traditional services, and they can also provide new service forms and methods. 2) Technology is an indispensable part of digital libraries. The development of the digital library involves the continuous application of information technology. Traditional technology platforms can be improved by technology required for big data processing, such as data acquisition, storage, analysis, and mining technologies. New technology solutions, such as distributed frameworks, parallel computing, big data, and artificial intelligence, will be a foundation of ongoing digital library innovation. 3) Service can be understood as a process in which a digital library can provide data resources directly or indirectly to users. It can also reflect the values of the application of technology in a library. In the big data era, it is possible to identify individual interest patterns of users such that services can be adapted to the changing information needs of users. Therefore, a traditional one-to-many service mode will gradually evolve into a more personalized one-to-one service mode. As a result, each user will have their own digital library, and the digital library can provide proactive services, such as personalized recommendations according to the user's interests. At the same time, we consider the possibility of user access to multi-device terminals to improve and enhance service levels in all aspects. Visualization allows users to access digital library services in a more intuitive and convenient manner. In future, various technologies are expected to become available, such as virtual reality and wearable
The user is the most important target of library services. In the past, we put forward the “customer first;” however, true action is not enough. Therefore, to enhance user satisfaction and improve existing service processes and methods, the perspective must be a primary consideration.
Conclusion It is worth stressing that, relative to traditional and digital libraries, users are not only the target of the service, i.e., users are also a valuable resource if standing on the side of resource. The big data era allows the possibility to fully understand and connect users. Interaction between users and libraries is not only to meet the information needs of users but also continuously provide more user resources to libraries. By exploiting user resources, digital libraries can have a broader perspective of the construction of data resources. Obviously, this user-centric digital library transformation model can provide good opportunity for personalized service development. This change in the characteristics of library services can put forward higher requirements of utilization of big data, and guide the direction of changes in library services. 28
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Acknowledgments
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PROBLEMS AND PROSPECTS OF IMPLEMENTING CLOUD COMPUTING IN UNIVERSITY LIBRARIES
Library Review Problems and prospects of implementing cloud computing in university libraries: A case study of Banaras Hindu University library system Mayank Yuvaraj,
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Problems and prospects of implementing cloud computing in university libraries A case study of Banaras Hindu University library system Mayank Yuvaraj
Cloud computing in university libraries 567 Received 31 January 2015 Revised 16 June 2015 Accepted 13 July 2015
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Central University of Bihar, Patna, India Abstract Purpose – The paper aims to examine the characteristic elements of various organizational factors to identify whether a favourable climate for implementing and sustaining cloud computing in Banaras Hindu University library system (BHULS) existed. Design/methodology/approach – After reviewing relevant literature on the topic, a list of 20 factors that affected an organization’s adoption to innovation was prepared. A questionnaire was personally administered to the library professionals engaged in BHULS. Respondents were asked to nominate the level of importance of each factor for validation of cloud computing adoption. Findings – Findings of the paper validate the fact that opportune time for the implementation of cloud computing existed in BHULS. Library staff showed high willingness toward the adoption of the cloud computing and were well prepared to grasp the challenges. Practical implications – The paper establishes the fact that the benefits of cloud computing are inadequate to convince organizations to migrate from the traditional computing paradigm to the cloud. Technological advancement may not transform the cloud into a mainstream technology. To motivate the expansion of cloud computing adoption, emphasis has to be laid upon collaboration between the cloud service providers supplemented by solid cloud legislations which need to be worked out. Originality/value – Because no empirical study on the cloud computing in conjunction with academic libraries of India has been carried out before, this paper closes this gap and provides guidelines to migrate to cloud environment. Also, it provides the perceptions of library professionals in response to its adoption. Keywords Cloud computing, University libraries, Banaras Hindu University library system, Cloud computing in libraries, Cyber library Paper type Research paper
Introduction Cloud computing has led to an increasing amount of literature on its theoretical concepts in recent decades. It is a form of computing that is based on the principle of sharing computing resources in lieu of having a personal dedicated server. Cloud computing involves the renting of computing resources on a metric scale, where enormous computing resources are supplemented by virtual data centres. Data centres are at the forefront of the back-end of the cloud computing niche. These data centres within themselves store immense potential to drive computing requirements. A plethora of services as an offering have come into existence within recent years, which are dedicated to different aspects of computing needs.
Library Review Vol. 64 No. 8/9, 2015 pp. 567-582 © Emerald Group Publishing Limited 0024-2535 DOI 10.1108/LR-01-2015-0007
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Cloud computing services are in a general way prefixed by “as a service”. The term is used as a prefix to connote the range of services offered on the cloud computing platform. The cloud phenomenon is a derivative of computer networking where the cloud symbol represented the Internet, and as cloud computing largely involves Internet-based computing, it came to be known as cloud computing. Cloud computing has often been criticised for being closely related to many symbiotic terms, such as autonomic computing, grid computing, ubiquitous computing, utility computing, etc. More or less, these terms are closely related to each other; they have a common objective, yet they are a part of the cloud landscape. Although the available literature unanimously considers that benefits lie in store with cloud computing for academic libraries, none to date have examined the perspectives of cloud computing implementation in libraries. This study is an attempt made in this direction, targeted towards assessing the implementation of cloud computing in the Banaras Hindu University library system (BHULS). Exploring the apprehensions and attitudes of the library professionals will help to find out the level of their understanding and interest in cloud computing as well as the level of its implementation. Cloud computing Cloud computing definitions are a work in progress. It has been a contested project that has been interpreted differently by different people. Winding up the concept, asking one question leads to 10 more. In view of the lack of agreement of interpretations, it would be fair to examine some of the existing definitions to clarify the term and what it involves (or might involve). According to Tadwalkar (2009), cloud computing derives its name from “Cloud” which represents data centres, technologies, infrastructure and services delivered through the Internet. Molen (2010) also argues that the term “Cloud” originated in the telecommunications world where telecommunications networks and the Internet were visualised on technology diagrams as clouds, signifying areas where information was moving and being processed, without the average person needing to know exactly how that happens. However, Kennedy (2009) opines that the term “cloud” is used in this system to include things like virtual servers, as it becomes a little difficult to point to exactly where all your data are being stored or managed. On the other hand, James (2008) argues that “cloud” is the content bazaar of the web. Stroh et al. (2009) asserts that cloud computing is nothing more than the collection of computing software and services that can be accessed via the Internet, rather than residing on a desktop or internal server. Whereas Joshua and Ogwueleka (2013) pronounce that cloud computing today is the beginning of “network based computing” over the Internet in force. It is the technology of the decade and the beginning of the end of the dominance of desktop computing such as that of Microsoft Windows. Over and above, the most cited NIST (National Institute of Standards and Technology) definition of cloud computing (Mell and Grance, 2009) reveals that cloud computing is a model for enabling: […] convenient, on-demand network access to a shared pool of configurable computing resources (e.g. networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction (p. 2).
While, according to some scholars, portraying relief features of cloud computing is as difficult as attempting to capture a genuine cloud with one’s hands.
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Although there is lack of consensus in defining cloud computing yet, the available literature shows an agreement over the 5-4-3 model (Furht, 2010; Rajaraman, 2014; Yuvaraj, 2015). 5-4-3 stands for five characteristics, four deployment models and three service layers. Characteristics There are five generally accepted NIST characteristics of cloud computing (Buyya et al., 2009; Furht, 2010; Marston et al., 2011; Mell and Grance, 2009; Qian et al., 2009; Wang et al., 2010; Yuvaraj, 2015): (1) Automated self-service setup: Users can run and configure their own computing resources as required. (2) Broad network accessibility: Cloud computing resources are universally available through Internet and accessible via various devices. (3) Pooled resources: Users do not have their own dedicated hardware and multiple users can work on the same hardware and resources. (4) Scalability: Users can keep control over the availability of computing power to them. (5) Metered services: Use of cloud resources is monitored and users have to pay for the amount of computing resources used. Deployment models There are four ways of deploying the cloud computing infrastructure (Furht, 2010; Hofer and Karagiannis, 2011; Hurwitz et al., 2009; Rajaraman, 2014): (1) Public cloud: They are like “wild animals” wandering of their own will in the cloud. Their implementation is shared by the general public. The cloud service provider sells or offers freely the cloud services on pay-per-use basis. (2) Private cloud: They are the “pets on leashes”. The private cloud implementation stays within a firewall that is restricted to its internal users. (3) Community cloud: They are owned, shared and supported by a specific community which have common objectives and concerns. (4) Hybrid cloud: This integrates the benefits of both public and private cloud to create a “super-breed” hybrid cloud. The cloud infrastructure enables data and applications portability. Service models The term service in cloud computing is the concept of being able to use the reusable, fine-grained components across a vendor’s network which is widely known “as a service” (Velte et al., 2010). There are three possible service models of cloud computing which have been divided according to the provided capabilities (Rajaraman, 2014; Sultan, 2010; Yuvaraj, 2015): (1) Infrastructure as a Service (IaaS): IaaS allows users to use their own hardware capabilities (such as storage, network, computing) to deploy a complete IT offering. It is ideal for those who want to avoid the hardware side and have little knowledge of configuring software. IaaS offers libraries the necessary infrastructure (electronic storage) that complements open source software
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(DSpace, Eprints, Fedora) or hosted software packages (Digital Commons, SimpleDL) for running digital library repositories and archives. (2) Platform as a Service (PaaS): PaaS offers a complete computing platform (operating systems, tools, applications) in the cloud. It enables users to develop, test, deploy, update and host services in the cloud landscape. It is a situation in which the necessary tools and applications are already made available in the cloud. For example, integrated library systems (Koha, Greenstone), GoogleDocs, WorldCat and subject catalogues are some of the library platforms in the cloud. (3) Software as a Service (SaaS): SaaS offers users the capability to use the software which is running on the cloud infrastructure. Users access this software through web browsers. Social networking services, web-based conferencing and web analytics are best examples of SaaS. In libraries, commonly used SaaSs are the Ebsco discovery services, citation management software, Libguides, etc. Literature review Cloud computing has become an attractive option for organisations, such as libraries, which would prefer to concentrate more of their focus and funds on their core mission instead of on IT issues (Wale, 2011). However, the implication of cloud computing in libraries has been an unresolved area of debate and concern in the library profession. Moving from “ground to the cloud” is surrounded with ambivalence, whether cloud computing offers the best solution to serve the user needs or not. There has been an abrupt change in the approach of library patrons to information accessibility and delivery that have actively moved into the virtual environment. Smartphones, mobile phones, tablets and laptops are everywhere now. Libraries therefore need to deliver resources and services in the virtual environment preferred by students, researchers, staff and faculty members, or they risk alienating users. To keep pace with progress, libraries need to switch over to cloud and deliver content, tools and services accessible to mobile users via mobile devices. Liu and Cai (2013) argue that shifting library core applications to cloud-based services will reduce or eliminate most or all of the local technical needs in managing server hardware and operating systems that underlie the applications. Moreover, according to Wale (2011), cloud computing brings along economies of scale and can help to make overall prices far more affordable for computing, storage, networking, preservation and administration. Most important to add is that cloud computing not only benefits individual end users and companies, but also attracts libraries in many ways when they must cope with budget cuts and constrained financial resources (Liu and Cai, 2013). Further, the implementation of cloud computing can enable more energy-efficient use of computing power, especially when the users’ predominant computing tasks are of low intensity or arise infrequently (Baliga et al., 2010). Supplementing the above arguments, Marston et al. (2011) feel that the impetus for change is currently seen predominantly from a cost perspective, as organisations increasingly discover that their substantial capital investments in information technology (IT) are grossly underutilised. Cloud computing enables new streamlined workflows for cooperation and community building among libraries (Goldner, 2011). According to Sultan (2010),
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cloud computing can provide colleges and universities with a means to upgrade software and IT hardware, attracting students and keeping pace with digital technological developments. On the other hand, Scale (2010) puts forth his view that cloud computing is currently enabling librarians to shift from the paradigm of ownership and maintenance of resources towards the provision of access to information maintained and controlled by others. However, cloud-based services are not entirely plug and play and libraries using cloud computing services need to worry about local bandwidth, hardware clients (PCs) and software configuration (Prince, 2012). According to Sorensen and Glassman (2011), cloud-based applications offer libraries new ways to present information or offer services that were previously unaffordable or unavailable. Patel et al. (2012) listed four core areas of cloud computing solutions in libraries: technology, data hosting archives, information and community. Simultaneously, various scholars have argued that cloud computing was already in practice before the concept gained momentum and there are ample possibilities in the future. For instance, Hoy (2012) asserts that many library patrons are already using cloud products, such as Gmail, Google Docs and bibliographic management tools, for their daily needs. On the other hand, Cohn et al. (2002) opines that libraries use database vendors or integrated library system providers who provide external servers to host library software and data in the cloud. Romero (2012) argues that in the field of library automation, there are several commercial suppliers already offering various adaptations of their products which make the use of the cloud possible to a lesser or greater extent. According to Prince (2012), some of the cloud-based options for libraries include IaaS- or PaaS-hosted ILS systems, in which libraries buy their ILS software from one vendor and host it on another vendor’s servers. Major ILS vendors exclusively having SaaS deployment options for libraries are: ExLibris, VTLS and Cyber Tools. Commenting on the future prospects of cloud-based library services, Wale (2011) argues that discovery tools can be embedded in commonly used applications, such as course management systems and institutional portals, enabling libraries to meet the needs of users wherever they are. According to Luo (2013), virtual reference services and research guides can be provided in libraries through software such as LibChat, QuestionPoint and LibGuides, which are all hosted on the cloud. Also, librarians in many developing world libraries are disadvantaged due to encountering connectivity challenges because funding cloud computing platforms or enhancing bandwidth are not always priorities in these regions, as there are more immediately urgent problems for funders to deal with, such as hunger (Mavodza, 2012). To develop a cloud-based library, there is a need of librarians’ training and practice to address the issues of cloud in reality. However, the biggest impediment to adoption of cloud computing is the lack of functionality of traditional counterparts in cloud computing (Marston et al., 2011). Cloud computing has been billed as a hot growth across various disciplines and various avenues have been explored theoretically for its practical application. Plenty of literature is available on the advantages, scope and areas of application of cloud computing in academic libraries but none, until now, have explored the preparedness of any academic library system for the adoption of cloud computing technology. This paper is an attempt to fill this void and to investigate the preparedness of the BHULS for the adoption of cloud computing.
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Banaras Hindu University library system The BHULS is a large university library system in India which comprises 4 institute libraries, 8 faculty libraries, 1 college library, 1 campus library and about 30 departmental libraries. It began with a small but precious donation made by P.K. Telang, billeted in the Central Hindu College (Kamachha). In 1921, the library was shifted to the present establishment in the Arts College. Through the benefaction made by Maharaja Sayajirao Gaekwad (Baroda), the library was moved in 1941 to its current splendid location. At the outset, the library started with a diminutive yet valued collection provided by such donors as the Tagore family, the Nehru family, Jamanlal Bajaj, Lala Sri Ram, Roormal Goenka and Batuk Nath Sharma. The trend of the donation of family as well as personal collection to the library continued until the late 1940s, resulting in an unparalleled in-house collection of rare books and journals. Collection and services Table I lists the library collection at BHULS. Conceptual framework Although the advantages of cloud computing have been established, adoption rates do not seem to be as high as they could be (McKendrick, 2012). A survey carried out by Tata Consultancy Services shows that in spite of the hype, the adoption of cloud applications presents a dismal picture – which is expected to increase significantly in the coming years. The adoption of cloud applications is still in the minority of all applications (19 per cent of average US companies, 12 per cent in Europe, 28 per cent in Asia-Pacific region and 39 per cent in Latin American companies) (TCS, 2012). It emphasises the fact that even though savants and decision makers show their consensus on the advantages of cloud computing, there are still a few issues that hinder its adoption by organisations. Organisational implementation of any new technology depends on several factors. Several factors are responsible for the successful implementation of cloud computing in libraries. From the review of the available literature, some common
Collection Books Journals (Bound volumes) Current journals PhD theses Manuscripts UN and government publications Staff publications Rare and out of print books Local history collection University and its founder collection Table I. Library collection Online journals and services at BHU, Databases Varanasi E-books
Services 1,060,269 1,57,062 530 16,500 7,233 3,632
11,142 10 41,738
Reprographic service Internet facility Online access to select journals Database search through DELNET INFLIBNET Electronic document delivery service Cyber library services Special reading facilities for physically challenged
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factors were used to carry out the study. These factors were: organisational factors, environmental factors, technological factors, data portability and security factors. Organisational factors incorporate the attitude of the organisation to the adoption of an innovation. Environmental factors constitute the attitude of the work culture as well as the support provided by the higher authorities in response to any new initiatives. Technological factors include the infrastructure as well as skills to adapt to an innovation. Moreover, as library services are heavily reliant on plenty of data available in the library, it is a big concern to migrate to a new platform added with security concerns. Objectives of the study The study was driven with the objective to identify the factors that are critical to the implementation of cloud computing in university libraries. For the purpose of the present study, the BHULS was selected, with the aim of investigating whether favourable conditions for the implementation of cloud computing existed or not, by evaluating the perceptions of library professionals in terms of: • Willingness of library professionals facilitated with organisational cultures to adapt to cloud computing technology. • Organisational culture that constantly motivates and provides incentives for continuous innovation. • Opportune IT infrastructure that facilitates the adoption of cloud-based technology. • Relevance of BHU Cyber library in the adoption of cloud-based technology. Methodology Research methodology defines the means of carrying out different types of research that are concerned with the overall strategy that a researcher chooses. The purpose of the present study was to explore the problems and prospects of implementing cloud computing in India through the vantage point of librarians engaged in BHU. An exhaustive review of the literature related to cloud computing and library and information services was observed to identify the fundamental demeanour of the connection between cloud computing and academic libraries. The modus operandi of the research incorporated a literature review, accompanied by a questionnaire survey to collect data and opinions from the librarians engaged in BHU. A questionnaire consisting of 18 open-ended and closed questions was designed to collect the required data for this study. In all, 50 questionnaires were distributed to the library staff of BHULS. Table II presents a snapshot of the respondents of BHULS who were part of the survey. Of the 50 respondents, 4 were deputy librarians, 8 were assistant librarians, 14 were professional assistants and 24 were semi-professional assistants who were purposefully selected due to their involvement in IT-based library services. The questionnaire was personally administered, as a result of which the response rate was 100 per cent. The majority of library staff participating in the study was aged between 25 and 45 (76 per cent). The maximum time of employment of the staff was between 2 and 25 years, out of which the majority of respondents (75 per cent) had between 2 and 18 years of experience in library service. Nonetheless, when
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respondents were asked about their qualifications, about 75 per cent of them had a Master’s degree in library and information science (LIS), 15 per cent a Bachelor’s degree in LIS and 10 per cent a Doctoral degree in LIS. Key findings from the survey When the respondents were queried about their organisations’ willingness to adopt innovative technologies (Figure 1), the majority of the library professionals (77 per cent) responded positively, citing various measures adopted by the library over a period to improve the library services, effectively catalysed with the resilience to adopt technologies to improve the available information and human resources. On the other hand, about 23 per cent felt that a lack of recognition and receptivity to change were the major constraints to any new development in the present setup. When asked about their work environment and its support of innovation (Figure 2), 47 per cent of the participants considered that it was an environment which encouraged the adoption of innovative technologies to keep pace with progress and efficiently deliver the library service. Of all, 29 per cent mentioned collaborative effort towards innovation rather than a personal endeavour and 15 per cent mentioned teamwork.
Library Table II. Distribution of respondents
No. of questionnaires
Sayaji Rao Gaekwad, Central Library Institute of Medical Sciences Library Institute of Agricultural Sciences Library Institute of Information Technology Library
40 4 2 2
23%
Yes
Figure 1. Willingness to the adoption of innovative technologies
No 77%
9% 15%
47%
Encoragement through BHULS administration Collaborative effort
Figure 2. Work environment support to innovation
29%
Team work Personal endeavour
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With respect to the ways in which staff performance was encouraged in their organisation (Figure 3), the majority of the participants (57 per cent) considered that performance was insufficiently rewarded or not encouraged at all. On the contrary, about 19 per cent of the respondents felt that material reward was offered to them as an encouragement, 14 per cent cited career advancement and 10 per cent quoted appreciation from senior fellows used for encouragement in lieu of their performance. When asked whether their organisation provided support for professional training courses or workshops (Figure 4), an overwhelming majority (69 per cent) agreed that they were encouraged by the organisation to participate in any conference or related event that was targeted towards modernisation of library services. Of all, 14 per cent pointed out that they took initiatives on their own to take part in such events. Over and above, 17 per cent claimed that BHULS conducts such events on its own to train their professionals, which highlights the sincerity of the BHULS administration, who understands very well the usefulness of a well-trained staff complemented with the newest technologies in library sector. When they were asked to mention the areas where they would like to gain more knowledge to overcome future challenges (Figure 5), 60 per cent mentioned knowledge of emerging technologies and their application to the LIS domain, 21 per cent mentioned knowledge of e-resources, library automation and digitisation and 19 per cent mentioned multi-lingual library services. To know about the administrative structure of BHULS, respondents were asked to describe their administration and management model (Figure 6). About 26 per cent of the respondents described it as providing an encouraging environment. Expressions such as innovativeness, lively, supple, egalitarian, candid and emulously character were used to represent the BHULS structure. On the other hand, 49 per cent showed a hostile
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19% 14% 57%
Material reward Career advancements Appreciation from seniors
10%
No rewards
Figure 3. Encouragement of staff performance
14%
69%
17%
Personal initiatives Organization self initatives Motivation from organization
Figure 4. Organization support to innovation
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attitude towards the administrative structure and characterised it as commanding and used expressions such as rigorous, tyrannical, muddled, repressive, opaque and taciturn in response to any mutation. Of all, 25 per cent did not answer this question or chose “not sure” from the questionnaire. The varied interpretations support the fact that the BHULS administration has not undergone any mutation. When asked about the BHULS policy towards library staff and their development (Figure 7), 57 per cent of the participants perceived the priority of the library in terms of organisational development to be orientated towards continuous professional development. Additionally, 8 per cent of the respondents remarked that BHULS gave more preferences to hiring staff with higher academic credentials. Of all, 30 per cent preferred not to say anything about the organisational policy of BHU. Although technology is essential for the success of cloud computing, the literature also reveals that technology alone does not ensure successful cloud computing. BHULS is equipped with the latest technology to store and disseminate information resources to their users. The library recently installed software to integrate information and
21%
E-resources
60%
Multilingual library services Emerging technologies and its application in LIS
19%
Figure 5. Interest areas of library professionals
25%
26% Encouraging Hostile Not sure
Figure 6. Administrative structure of BHULS
49%
3% 2% Did not say
30%
Figure 7. BHULS policy towards library staff and their development
57%
8%
Hire staff with higher academic credentials Positive attitude Negative attitude Motivate higher studies and skill development
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knowledge of the resources and users of different sections. To bridge the gap of digital libraries, a high-powered computer laboratory for accessing e-resources referred to as Cyber library has also been established. Expert and best practice databases, portals and knowledge repositories have not yet been designed and maintained by BHULS. However, most of the participants (79 per cent) utilise the Internet, Google Apps and Web 2.0 tools to share knowledge and to keep themselves abreast of the latest developments in their field. Asked about their understanding of the importance of cloud computing technology (Figure 8), the library staff mentioned that the adoption of cloud computing in library services would remove their IT obligations and they could focus on delivering services only. When asked about their usage of cloud computing applications, the majority of the staff (69 per cent) again responded positively, citing various cloud computing applications such as Google Docs, Box, Amazon Elastic and Skydrive which they were using in their daily work. Figure 9 presents a summary of various cloud-based tools used by the respondents for their daily work. In response to the question regarding the motivation of adopting cloud computing technology in BHULS, the following reasons were mentioned by the respondents (Figure 10): professional cooperation and work efficiency through sharing data in the cloud (18 per cent), enhanced focus on library services (14 per cent), ubiquitous services accessibility (33 per cent) and location and device independence (11 per cent). Use of Internet, web 2.0, Google Apps, Cloud based tools
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High Computing Power
21%
Location and Device independency High Scalability
79%
Less maintenance Use
Do not use
Less indulgence in library IT activities Unlimited storage capacity Diverse support
6% 10% 11%
7%
8% 6% 9%
9%
5%
3%
5% 16%
5%
Faster deployment and development Greener library services Ubiquitious availability Pay-per-use Reduced technology obsolence No capital investment
Figure 8. Importance of cloud computing in libraries
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Cloud-based mailing services Cloud-based storing services Cloud-based software and applications
578
4%
12%
Cloud-based video and presentation services
16%
Cloud-based file sharing services
8%
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12% 6%
Cloud-based information collection services Cloud-based calendar services Cloud-based social networking
9%
8% 6% 7%
Figure 9. Use of cloud based tools
3%
10%
Cloud-based forums Cloud-based operating systems Cloud-based office applications Do not use
Figure 10. Motivating factors for adoption of cloud computing in libraries
Among the respondents reluctant to adapt to cloud computing technology (Figure 11), the lack of rewards and incentives (21 per cent), fear of negative consequences with the adoption of cloud-based software (16 per cent) and insecurity about the data stored on third-party servers (63 per cent) were mentioned as reasons.
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When asked about the usage of any cloud-based tool in their organisation, the majority of respondents (68 per cent) were unclear about cloud-based software but agreed that they were using tools such as Google Apps in daily activities in the library. However, 10 per cent of the respondents mentioned that they have used cloud-based discovery tools to provide reference services, 15 per cent mentioned that the knowledge available to them was inadequate to work within the cloud computing environment and 11 per cent mentioned that they were satisfied with the traditional computing methods used in the library. When respondents were asked to indicate the knowledge requirements to move over to a cloud computing environment in the future (Figure 12), 30 per cent of the staff specified the requirement of IT skills, 7 per cent specified their willingness to enhance their knowledge level by searching for an alternative tool available in the cloud and 42 per cent specified no requirement to enhance their level of knowledge, as they are equipped with the IT skills to work in the cloud environment.
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Conclusion Although the role of organisational factors in the successful adoption of an innovation has been an established theory, the results of our research show that some of the elements of the organisational factors are existent and there are fairly favourable conditions for adopting cloud computing tools and software in BHULS. BHULS staff who took part in the study showed high levels of enthusiasm to work in the cloud
Figure 11. Reasons for unwillingness to adopt cloud computing
Figure 12. Requirement of skills for working in cloud environment
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computing environment and showed their preparedness to face the challenges. Cloud computing, when applied in a library setup, can improve library services and ensure a safe position for the library which is in question with the emergence of social media and the Internet. Most importantly, the benefits of cloud computing are inadequate to convince the organisations to migrate from the traditional computing paradigm to the cloud. Technological advancement may not transform the cloud into a mainstream technology. Most of the BHULS staff was not comfortable with the cloud from security and regulatory perspectives. With the adoption of cloud computing, an organisation completely loses its control over IT and data. Trust in the service provider, data portability, migration and privacy is the big concern when it comes to adopting cloud computing technology. The body for standardisation, the ISO (International Organization for Standardization), is in the process of developing a code of practice for information security controls based on ISO/IEC 27002 for cloud services. To motivate the expansion of cloud computing adoption, the emphasis has to be laid upon collaboration between the cloud service providers, supplemented by solid cloud-related legislation. However, this research is limited to BHULS and the findings of this research cannot be used to generalise to other university libraries in India. Future research should utilise a larger sample and examine more concrete issues with regard to organisational factors, which are critical to the success of the cloud computing environment in university libraries. References Baliga, J., Ayre, R.W.A., Hinton, K. and Tucker, R.S. (2010), “Green cloud computing: balancing energy in processing, storage, and transport”, Proceedings of the IEEE, Vol. 99 No. 1, pp. 149-167. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J. and Brandic, I. (2009), “Cloud computing and emerging IT platforms: vision, hype and reality for delivering computing as the 5th utility”, Future Generation Computer Systems, Vol. 25 No. 6, pp. 599-616. Cohn, J.M., Kelsey, A.L., Fiels, K.M. and Salter, D. (2002), Planning for Integrated Systems and Technologies: A How-to-do-it Manual for Librarians, Facet, London. Furht, B. (2010), “Cloud computing fundamentals”, in Furht, B. and Escalante, A. (Eds), Handbook of Cloud Computing, Springer, New York, NY, pp. 3-19. Goldner, M. (2011), “Winds of change: libraries and cloud computing”, Multimedia Information and Technology, Vol. 37 No. 3, pp. 24-28. Hofer, C.N. and Karagiannis, G. (2011), “Cloud computing services: taxonomy and comparison”, Journal of Internet Services and Applications, Vol. 2 No. 2, pp. 81-94. Hoy, M.B. (2012), “Cloud computing basics for librarians”, Medical Reference Services Quarterly, Vol. 31 No. 1, pp. 84-91. Hurwitz, J., Bloor, R., Kaufman, M. and Halper, F. (2009), Cloud Computing for Dummies, Wiley, Hoboken, NJ. James, S. (2008), “Is cloud computing ready for the Enterprise?, A client choice report”, Forrester Research, online behind a paywall, available at: www.forrester.com/Is⫹Cloud⫹ Computing⫹Ready⫹For⫹The⫹Enterprise/fulltext/-/E-res44229 (accessed 12 September 2015).
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1. QadriMaroof Naieem, Maroof Naieem Qadri, QuadriS.M.K., S.M.K. Quadri. 2018. Mapping cloud computing in university e-governance system. International Journal of Intelligent Computing and Cybernetics 11:1, 141-162. [Abstract] [Full Text] [PDF] 2. YuvarajMayank, Mayank Yuvaraj. 2016. Ascertaining the factors that influence the acceptance and purposeful use of cloud computing in medical libraries in India. New Library World 117:9/10, 644-658. [Abstract] [Full Text] [PDF]
PROMOTING INNOVATION AND APPLICATION OF INTERNET OF THINGS IN ACADEMIC AND RESEARCH INFORMATION ORGANIZATIONS
Library Review Promoting innovation and application of internet of things in academic and research information organizations Elisha Ondieki Makori,
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Article information: To cite this document: Elisha Ondieki Makori, (2017) "Promoting innovation and application of internet of things in academic and research information organizations", Library Review, Vol. 66 Issue: 8/9, pp.655-678, https:// doi.org/10.1108/LR-01-2017-0002 Permanent link to this document: https://doi.org/10.1108/LR-01-2017-0002 Downloaded on: 03 April 2019, At: 18:25 (PT) References: this document contains references to 58 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 1409 times since 2017*
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Promoting innovation and application of internet of things in academic and research information organizations Elisha Ondieki Makori
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Department of Library and Information Science, College of Humanities and Social Sciences, University of Nairobi, Nairobi, Kenya
Innovation and application of internet of things 655 Received 5 January 2017 Revised 11 April 2017 Accepted 3 July 2017
Abstract Purpose – The purpose of the study was to investigate factors promoting innovation and application of internet of things in academic and research information organizations.
Design/methodology/approach – Quantitative research design involved survey of selected academic and research information organizations in public and private chartered institutions. Information professionals, digital content managers, information systems and technologists that normally consume big data and technological resources were involved in the process of data collection using structured questionnaire and content analysis. Information organizations and information practitioners were selected from public and private academic and research institutions. Findings – Innovation of internet of things has increasingly transformed and changed academic and research information organizations as the source of knowledge in addition to expanding access to education, data, information and communication anywhere anytime through hyperconnectivity and networking. Internet of things technologies such as mobile of things, web of things, digital information systems and personal devices are widely applied by digital natives in academic and research information organizations. Mobilization platform and devices is the single biggest provider of data, information and knowledge in academic and research organizations. Modern trends in education and knowledge practices in academic institutions and information organizations depends upon internet of things, digital repositories, electronic books and journals, social media interfaces, multimedia applications, information portal hubs and interactive websites, although challenges regarding inadequate information communication technology infrastructure and social computing facilities still persist. Research limitations/implications – Information organizations in public and private chartered academic and research institutions were adopted in the study. Respondents handling and supporting information management, planning and decision-making provided the necessary data. Information professionals, digital content managers, information systems and technologists are proactively involved in data and information analytics. Practical implications – Academic and research information organizations are powerhouses that provide knowledge to support research, teaching and learning for sustainable development and the betterment of humanity and society. Innovation of internet of things and associated technologies provides practical aspects of attaining sustainable information development practices in the contemporary knowledge society. Internet of things technologies, principles of economies of scale and investment and customer needs entail that information organizations and practitioners should provide appropriate and smart systems and solutions. Social implications – Modern academic and research information organizations have the social corporate responsibility to offer technological innovations to heighten access to knowledge and learning in academic and research institutions. Economically, innovation and application of internet of things provide unlimited access to big data and information in organizations all the time anywhere anytime. Originality/value – Data management is a growing phenomenon that information practitioners need to fully understand in the digital economies. Information professionals need to embrace and appreciate
Library Review Vol. 66 No. 8/9, 2017 pp. 655-678 © Emerald Publishing Limited 0024-2535 DOI 10.1108/LR-01-2017-0002
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innovation and application of internet of things technologies whose role in sustainable development practices is critical in academic and research organizations.
Keywords Kenya, Internet of things, Academic institutions, Data and information management, Information organizations, Internet of everything Paper type Research paper
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Introduction and background information Digital and media development has created a tremendous impact on information and business organizations across the globe. In the global change movement, leading academic and research institutions and information organizations have embraced innovative technological solutions to support knowledge and learning needs. Notably, in the contemporary knowledge society, academic and research information organizations have undergone transformation in the search for relevance and quality delivery of services to the customers. Emergence of the technological platforms in transforming and broadening knowledge and learning in academic and research organizations has helped to redefine the evolving role of information professionals. Practical advanced use of technology has opened innovative practices for information professionals to manage knowledge in organizations – especially internet of things (IoT) or internet of everything (IoE). IoT refers to the use of intelligently connected devices and system to obtain data gathered by embedded sensors and actuators in machines and other physical objects (Groupe Speciale Mobile Association, 2014). IoT uses connecting media such as wireless sensor network and physical objects to connect devices to each other and the internet, with minimal direct human intervention to deliver services that meet the needs of a wide range of academic libraries, adds the author. In a nutshell, IoT is the process of connecting things and objects at homes, industries and work environment using intelligent technological systems (sensing, networking, connectivity, digital and media applications and internet platform) for real-time interaction and sharing of data, information and communication. IoT and related technologies are transforming and changing information management and learning in academic and information organizations, where smart systems and solutions are applied in homes, offices, hospitals, transportation, enterprises and factories (Lueth, 2015). On the same note, the forces driving the IoT and motivational benefits are increasingly numerous, as more and more organizations, industries and technologists catch the bug (Borne, 2014). Modern digital environment has created the IoT as a universal information and knowledge portal or hub for strategic value and competitive advantage in academic and research organizations. IoT and related information technologies are widely used for sharing, free circulation and transaction, and on-demand-use of resources and capabilities in different fields – industries, manufacturing plants and business organizations (Fei et al., 2014). Intelligence and innovation of the internet has invented digital technology applications perhaps than never before through social computing, social media, telecommunications and mobile communications. The dynamic and multiplicity nature of the IoT technologies reshaping the potential of academic institutions and information organizations has created renewed knowledge and innovation through mobile-learning (m-learning) for distance and e-learning programs (Ozuorcun and Tabak, 2012; El-Hussein and Cronje, 2010), mobile computer devices such as smartphones, tablets and e-book readers (Hashemi et al., 2011) and social media (Davis et al., 2012). Mobile computing technologies are widely used in academic institutions and information organizations to heighten access to knowledge and learning among students (Eden, 2012). Smartphones, tablets and e-book readers connect the students with information, knowledge and learning in higher education
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anywhere anytime, while social media platforms are applicable in political mobilization, information and communication, education and business aspects. Modern information, knowledge and communication environment is increasingly becoming intelligent through IoT technologies such as data mining, artificial intelligence, geographical positioning and robotic systems. Information, knowledge and technology are the key drivers for socioeconomic growth and prosperity in the society that promotes sustainable development practices and environment protection (Dzidonu, 2010). IoT technologies have become increasingly important in knowledge organizations through green information systems (cloud computing and green information technology [IT]). Information professionals provide hybrid knowledge whose connection to the internet is evident through enterprise information technologies and electronic resources (e-books, ejournals and digital theses and dissertations), websites and Google scholar. In the digital dispensation, information professionals have to identify potential application of IoT technologies as opposed to the traditional use of radio frequency identification (RFID) (OCLC, 2014). Information organizations need to adapt the right technological solutions and platforms for effective and efficient delivery of services to the customers. Evolution of the IoT as a fundamental innovation for transforming and changing the nature of information organizations in higher education and learning is indeed necessary in the contemporary sustainable development environment, although the magnitude of practitioners in planning and understanding the knowledge and educational needs in academic and research institutions formed the core aspects of the study. Context of the study Principle contribution of the study was to determine the extent to which information practitioners understand and manage knowledge and technological needs in academic and research organizations in the modern digital economies. Academic and research information organizations in public and private chartered institutions need to provide fundamental structures and resources to support research, teaching, learning and community services. Total of ten academic and research information organizations in public and private institutions with necessary technological infrastructure and professional experts were surveyed. Public higher education and learning institutions that are usually funded by the government had six academic and research information organizations, while four were from the private ones. Information professionals, digital content managers, information systems and technologists manage and provide information services. Digital content managers, computing experts and clients form the single biggest segment that normally consume data and technological resources. Information professionals equally provide central roles in strategic planning and decision-making that is critical in investment and utilization of resources. Statement of the problem Emerging digital economies and innovative solutions have made the IoT fundamentally essential for societal development that enhances social, economical, political and cultural inclusion. IoT platform provides ubiquitous and pervasive technologies with massive opportunities to data and information resources. Notably, development of IoT has invented smart technologies that are not only fundamental but also very powerful business tools and devices integrated and deployed in information organizations for mining and collaborating big data and information resources essential in planning and decision-making. IoT connects billions of objects, monitors, sensors, RFID and mobile devices with a projected estimation of 50-100 billion internet-enabled devices in 2020 (OECD, 2012; European Union, 2010),
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although the biggest technology challenge is the integration of everything including big data, cloud, M2M and the network fabric (Borne, 2014). In information organizations, the application and use of mobile devices whether smartphones, tables and earphones are a common occurrence and feature among the clients. Hyperconnectivity through networking and associated technologies to enable access anywhere anytime is increasingly growing in organizations. In the modern digital environment, academic and research information organizations depend upon technological solutions such as cloud computing, mobile devices to provide effective and efficiency services to the customers. With numerous technological innovations being produced, information organizations are at crossroads on the right and appropriate systems to use. Return on investment and effective utilization of finances are critical aspects in planning and decision-making process since IoT technological innovation and application need massive resources which most information organizations lack. With hard economic situations facing academic and research information organizations, IoT needs to support and enhance the goals and objectives of academic institutions for posterity and betterment of humanity and society. Market for IoT ushers unlimited opportunities that are exponentially growing and fundamental in the modern digital economies. Purpose and objectives of the study Purpose of the study was to investigate factors promoting innovation and application of IoT in academic and research information organizations. Specific objectives were to: examine the potential application of IoT in transformation of academic and research information organizations; explore the range of IoT technologies applied to heighten access to knowledge and learning in academic and research information organizations; determine the technologies used to influence the application of IoT in academic and research information organizations; and investigate practical strategies for enhancing effective management of IoT in academic and research information organizations. Research questions RQ1. How has the potential application of IoT transformed and changed academic and research information organizations? RQ2. What range of IoT technologies is applied to heighten access to knowledge and learning in academic and research information organizations? RQ3. Which technologies are used to influence the application of IoT in academic and research information organizations? RQ4. What practical strategies are used to enhance effective management of IoT in academic and research information organizations? Literature review Digital transformation and internet of things Digital information and communication landscape is characterized with innovative IoT technological systems, mobile of things, web of things (WoT) and hyperconnectivity. IoT has transformed and changed information organizations from the traditional pre-computing systems to the modern knowledge and communication portals. British entrepreneur Kevin
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Ashton coined the term “internet of things” in 1999 to describe the connection of the physical world and objects to the internet through sensors (Internet Society, 2015; Gubbi et al., 2013). IoT is the process where network connectivity and computing capability are applied to objects, sensors and everyday items not normally considered computers, allowing the devices to generate, exchange and consume data with minimal human intervention (Internet society, 2015) or the environment where every aspect of social and economic life including individuals, machinery, natural resources, production lines, logistics networks and consumption habits are linked through sensors and software to the internet platform, regularly feeding real-time data to relevant recipients such as businesses, homes, health-care providers, vehicles and supermarkets (Rifkin, 2014). The relationship involves people– people, people–things and things–things (Morgan, 2014). WoT is the integration of physical objects and virtual things into the World Wide Web (WWW) through multiple platforms of social computing and telecommunication systems. IoT provides information and knowledge organizations with digital connections, collaborations, communications, discussions and online transactions from cell phones, headphones, wearable devices and virtually all things. Innovation turns knowledge organizations into smart portals where customers can interact with various things and get virtually all kinds of information using devices with communication capabilities. Emerging digital integration and technological innovations provide the necessary platform for transforming the vision and reality of the knowledge society to achieve sustainable research and learning in academic and information organizations. In the digital economies, the IoT has transformed information organizations with innovative technological solutions that manage knowledge in academic institutions through systems such as data and information analytics and enterprise information management. Data and information analytics Digital transformation is driven with data and information resources that are captured, mined and utilized to support decisions. This has led to rapid production of big data and information explosion, making access to accurate and relevant resources increasingly difficult. Data and information have become critical resources that provide necessary metrics to make decisions in organizations. IoE consists of four key elements that are integrated for planning and decision-making – data, things, people and process (Bradley et al., 2013). IoT plays a significant role, as it offers the network of physical devices and objects connected to the internet for decision-making (Dawson et al., 2013). Analytics is the scientific process of transforming data into information for the purpose of planning and decisionmaking, while collection management is the activity of assessing, planning and supervising the growth and preservation of information collections and services (ODLIS, 2016). Data visualization supports assessment through information insights that are used to promote products and services, enhance effectiveness and efficiency and foster customer relationships. Collection management is driven by transformed data that support decisionmaking, promote marketing, budgeting, webometrics (citations of publications) and usage of information resources. In information organizations, patterns of download holdings and query resolutions that have major implications for discovery, selection, acquisition and management of collections are mined to improve services (Dempsey et al., 2014). Collections are changing in a network environment with data- and information-driven activities including research and learning behaviors, the authors add. This promotes patron-driven acquisition (demand-driven acquisitions) purchasing model where the patrons select e-books from the online public access catalog (OPAC) for possible purchase. Patron-driven acquisition model ensures purchasing of some e-books that are being used and collecting
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some statistics on the desires of the patrons (Richard, 2012). Information professionals use data visualizations to recognize trends, spot patterns and identify exceptions (Murphy, 2013). Powerful new tools for visualizing and distributing data, measurement standards for performance and potential for creating a robust canon of metrics are available for information professionals (Zucca, 2013). Libraries have been doing data visualization for a while using tools such as JavaScript, Excel, PHP and Google Charts (Murphy, 2013). Information intelligent dashboards are business visualization tools with the ability to pull real-time data from multiple sources. Dashboards allow librarians and management to monitor key information operations from a single, convenient page, with an emphasis on long-term trends rather than day-to-day fluctuations in use (Morton-Owens and Hanson, 2012). Tableau is the data visualization and analysis software that provides metrics on how to manage and support operations, functions and services in information organizations. The software (tableau) is increasingly used in libraries to better manage and present the large quantities of data collected to support decision-making (Murphy, 2013). Academic libraries, regardless of Carnegie designation, share a common mission to support the teaching and learning enterprise, and the fulfillment of that mission amid today’s pressures is increasingly linked to intelligence about resource consumption, service quality and the library’s impact on research and student learning (Zucca, 2013). Enterprise information management systems In the competitive digital economy, organizations are increasingly embracing enterprise information management systems to provide access to online information and knowledge services anywhere anytime through cloud, internet, hosted or web-based applications (Makori and Osebe, 2016). IoT systems such as social media, cloud and mobile computing applications produce big data that need to be mined and processed into information to support decision. Enterprise information management is the automation of information processes, functions and services to provide accurate and relevant information for decision-making, analysis and communication. The technological systems help information professionals to provide seamless automation services that enhance efficiency in management of information processes and functions – acquisitions, cataloging, circulation control, serials, research and reference support, OPAC and patron. Enterprise information management strategy provides strategies and technologies for access and utilization of digital collections of books, journals and local content; application of mobile devises such as iPads and personal digital assistants (PDAs); analytics (data aggregation and mining) for discovery, selection, acquisition and management of collections (Dempsey et al., 2014, pp. 11-12) as well as webometrics ranking. This practice influences product advancements and integration with e-procurement, e-payment, mobile billing and e-commerce applications in information organizations. As an enterprise approach to systematic decision support, the University of Pennsylvania libraries (Penn libraries) uses the MetriDoc to provide the IT infrastructure that facilitates the collection and transportation of data for planning and decision-making (Zucca, 2013). Information intelligence systems Intelligent information systems integrate artificial technologies, social computing, database methodologies and customer relationship management (CRM) to support business decisions in organizations. In information organizations, artificial intelligent systems provide the information applied in decision-making and maintaining sustainable competitive environment. Decision makers will have to look beyond transactional reporting systems to systems that provide intelligent information. Artificial intelligent systems have potential to
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change the way resources are utilized resulting in more efficient, smart and friendly systems (Kulkarni and Prachi, 2015). Information organizations use intelligent systems and robots for delivery of services, detection of theft and burglary, authorizing access and use of resources, payment of fines, information retrieval and indexing. To support decision-making and planning activities in the organization, analytical intelligent systems and tools monitor and control information operations and functions through data mining, assessment studies, customer relations management. This provides the convergence for data mining, information analysis and communication. Robotic systems use artificial intelligence and learning experiences to work and perform functions and operations in information organizations such as providing services to the clients, shelving and carrying of books and security. Once automated, unlike a computer or machine that performs a given task, typically, a robot not only performs the task given to it, but it is also able to use artificial intelligence and learn from its experiences while performing given tasks (Lakshmi et al., 2014). Competitive environment has also made information organizations to embrace the business focus of CRM so as to promote delivery of products and services. Application of CRM in libraries will add value to information services (Piyawan et al., 2011). Innovation and application of internet of things IoT technological trends in academic and research information organizations largely harbors around aspects of data and information management, automation practices and knowledge management although the innovation provides practical application and potential benefits as exemplified through Pujar and Satyanarayana (2015). Data and information driven Data, content, information and knowledge are powerful and essential assets used for planning and decision-making in organizations. IoT environment produces massive data and information that need to be captured, processed and managed for better decisions in information organizations. Big data and information explosion or obesity possible through business process, analysis, monitoring and modeling are critical in managing and supporting functions and operation across organizations. Data, content and information analytics are important applications for automation process and knowledge management. IoT makes use of powerful and hardest tools of data analytics and visualization, content analysis and intelligence, dashboard applications to support academic and research information organizations in needs assessment, collection management and automation process. Data management is about finding, mining and making sense and use of the right information at the right place in the right time using the right and smart devices – smartphones, tablets, mobile apps, laptops and wireless. Self service RFID used in conjunction with other applications makes loanable materials internet-enabled “things”. RFID tags allow the object to wirelessly communicate certain types of information, allowing libraries to provide information about the materials to those with the proper handheld device/application. Entrance and exit of library and information organization with RFID sensors issue warning signals that activate the security alarm platform if the tagged documents and materials leave the premises without authority of the system.
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Access and utilization Information organizations provide virtual cards to the clients with mobile applications so as to gain access and utilize the available products and services. This helps the clients to access, identify, locate and use the required information resources. It also provides further information about the resources by connecting to the site like Amazon so that the client has detailed information to make an informed decision about the resource before borrowing. Patrons with wearable devices could wear the “library cards” to the library whereby scanning at the self-service stations gives the opportunity to access and use the computers and related resources. Collection development and management Cloud and mobile computing improve the services of efficiency and visibility of collection development and management. Virtual representation of information materials with RFID tags and readers enhances collection development and management practices. Smartphones with IoT informs clients on the total amount on overdue materials and also provides the pay fine online without having to physically visit the library circulation desk. IoT also helps in better stock management (stock verification), as it makes it easy to locate misplaced information materials. Using pressure pad sensors in the aisle under the floor is another innovative technology that helps in collection development in addition to improving signage that might be required and automatic turn on/off of light bulbs for energy saving making a smart library. Reference services Virtualizations of information resources help reference librarians to rank the preferred materials as well as determine the best approaches to promote and market the underutilized ones. Data visualization provides information on utilization of materials that, in turn, aids in planning and decision-making. Information literacy Information literacy skills and competencies are offered to clients on how to ensure proper utilization of knowledge resources and services for research, learning, critical thinking and problem-solving. IoT helps information organizations and professionals to provide selfguided virtual tour. Libraries and information centers with virtual beacons like wireless devices provide clients with smartphones with the opportunity to play video or audio explaining more about the section and how one can get maximum benefit out of it. In addition, it provides enriched experience of special collections like manuscripts by offering digital format on the smartphones as physical access to such resources is restricted. Location and instant alert services With IoT, it is possible for the clients to get instant and update alerts on information products and services. IoT uses real-time data based on the history of the client to inform and suggest or recommend favorable information products and services, for instance, new arrivals, materials reserved and location where resources are shelved. With IoT-enabled smartphones, the clients get instant notification or alerts on the available information materials and their exact location. This practice enhances the security and control of information materials and other physical assets in the organization. With magic mirror technology senses, clients are also informed of interesting titles available on the topics and status of the holdings plus getting recommendations on other similar materials and
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mentions of related events. This also informs on the status and availability of reading and discussion rooms, printers, scanners and computers by displaying the peak and non-peak hours of usage on the library website or clients can check it using the library mobile app. The attendance management system with RFID assists to alert the students at the entrance of the classroom on daily basis. If a student fails to return or renew the material, the attendance module will not register the entry and is alerted by voice message. Appliance management IoT helps information organizations, staff members and clients to enhance better management and control of the available appliances resulting to saving the resources and energy costs (Frenzel, 2012). A client walking into the library using a cubicle or reading table with IoT-enabled mobile phones would be able to control the lighting, air conditioning and Wi-Fi. Personal devices such as smartphones and laptops can be used to communicate the status of information organizations, classrooms and computer laboratories. IoT as the emerging technology provides new effective and efficient services in academic and research information organizations. This, in turn, has made library buildings “smart organizations” where clients interact with various things and get virtually all kinds of information and communication capabilities and opportunities using IoT devices. Marketing automation Mobile customer engagement, geolocation and Apple’s iBeacon are all creating a network of knowledge about locations, intentions, preferences and buying patterns. IoT technologies have created smart and quick methods for libraries to market information products and services within and beyond the geolocation and boundaries of the clients. Of course, this degree of location-based knowledge needs to strike the right balance between user privacy and the timely delivery of useful products and services (Borne, 2014). Internet of things technologies Enterprise resource planning systems Digital information environment provides intelligent technologies for data mining, artificial intelligence, geographical positioning system and IoE. Information and knowledge organizations have no choice but to provide not only quality services to the clients but also to remain relevant while maintaining the competitive advantage in the marketplace of information and knowledge. The application and introduction of enterprise resource planning (ERP) systems have become a central issue for management and operation of organizations to increase efficiency, effectiveness and better utilization of resources (Molnár et al., 2013). ERP systems enhance productivity and working quality by offering integration, standardization and simplification of multiple business transactions (Dimitrios et al., 2011). Koha has gained momentum across the world as the best ERP system for automating and integrating information management practices due to the development of the internet that fundamentally makes free and open-source software programs easily available (Makori and Osebe, 2016) and whose return on investment is pocket friendly. Cloud of things: cloud computing and RFID In the modern digital and innovation landscape, information and knowledge enterprises are rapidly adopting and deploying highly advanced information and communication technology strategies such as cloud computing and social computing. Cloud computing is a kind of computing application service just like email, office software and ERP that uses
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ubiquitous resources to be shared with the business employees or trading partners (Chinyao et al., 2011). As the integrated and distributed social IT model, cloud computing uses highly advanced networks of hardware and software systems to provide and deliver services to the client through the internet. With cloud computing, mobile computing interface provides access to big data and information resources in organizations needed for planning and decision-making. Service providers offer various types of cloud computing such as public, private, community or hybrid. Cloud computing or internet-based social computing in information and knowledge organizations promotes resource sharing, insecurity and control, return on investment, scalability and space crunch (Romero, 2012). In information organizations, cloud computing integrates and uses RFID for automation processes to manage big data and information resources provided from the tagged materials. RFID technology uses radio waves to monitor and control provision of information resources and services. Identification and tracking technologies applied in IoT include RFID systems, barcoding, intelligent solutions and sensing. Typically, RFID manages collection development, inventory control and assets where data and information are mined, harvested and analyzed to provide seamless services in organizations. Data from sensors are acquired and integrated for analysis, decision-making and storage (Li et al., 2015). Rapid development of social media applications has increased cloud computing together with RFID systems where clients are able to access information services using mobile devices enabled with IoT. Mobile of things In the high-tech digital dispensation, rapid demands in the information market has encouraged the need for mobile innovation in enhancing access and utilization of resources in knowledge organizations. Mobile and smartphone applications create huge amounts of data and information that need to be correlated, analyzed and integrated for decisionmaking. IoT innovation has transformed the modern information and media landscape into the global knowledge economy where mobile devices including laptops, net books, notebook computers, cell phones, audio players (MP3 players and cameras) and other items are widely used in library and information centers. Notably, many academic libraries provide loan programs for laptops, cameras, video cameras, MP3, audio players, headphones and internet-capable devices (iPod touch). Mobile ubiquitous technology provide powerful and indispensable tools for study, productivity and task management that have been integrated and used just like with car keys and wallet (Johnson et al., 2010). Virtual academic education and learning, access to quality information and sharing of knowledge are possible through innovative mobile-learning that uses PDAs, iPods, mobile phones, smartphones and tablets (Ozuorcun and Tabak, 2012), electronic and web resources (Wordofa, 2014 and Lwoga, 2014). Studies highlight that, in a knowledge community, many academic library young generation users frequently visit internet and have some kind of active presence on social network sites, and prefer new technologies for information and intellectual exchange because of their convenience and speed (Srinivasa and Brahmaiah, 2016). Libraries are utilizing interactive social media tools for information services such as blogs, Facebook, WhatsApp and Twitter. Libraries and information services use mobile technology on the cloud environment to support the information needs of users. This is because mobile devices are running increasingly complex software and applications, allowing advanced user interactivity, interaction with cloud services and able to run rich multimedia content. The use of sophisticated mobile technologies such as Bluetooth, smartphone software applications, Wi-Fi and global positioning systems are all making mobiles for multipurpose
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use “expanding the capabilities of teaching and learning, providing access to rich multimedia resources and student centered applications” which enables libraries and knowledge agencies to provide scale information resources in real time (Vollmer, 2010). Having the right devices at the right place in the right time is indeed critical and fundamental for information professionals, clients and organizations.
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Information discovery and insight IoT provides powerful intelligent tools for mining and harvesting data and information and distributing the resources in organizations through sensors, actuators, knowledge portal, automation and enterprise systems, CRM, digital repositories and electronic resources. The range of smart IoT devices found in institutions of learning includes e-books and tablets; sensors in hallways, entrances, classrooms, offices and institutions’ vehicles; all manner of fitness bands and wearables; headsets; video sensors; robots; lights and many more. Data from these devices can be used for tracking, for instance, campus shuttles, student attendance, supplies and understand students’ learning patterns (Asseo et al., 2016). IoTenabled applications provide the platform for collecting and gathering data sets of information across the departments, organizations and partners for strategic planning and decision-making. Technologies for searching and discovering information and knowledge resources in organizations are useful in saving the time and energy of the stakeholders. With the development of the IoT, discovery services and tools facilitate real-time search and access to multiple information sources simultaneously from a single platform for efficient utilization and retrieval. In academic and research information organizations, knowledge discovery and searching services facilitate access to electronic resources such as e-journals, open-access journals, e-books and digital repositories.
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Web of things Emerging digital innovation in web and media development with hyperconnectivity technologies to provide multiple platforms for mobile devices, laptops and even wearable displays has dominated information and knowledge landscape across the globe (Dawson et al., 2013). With rapid hyperconnectivity information, organizations have the opportunity to provide real-time and instant access to knowledge and learning. WoT as the new ubiquitous computing of technological platforms has increased big data and information explosion coupled with the challenges of processing, storage and managing that face organizations. The development of new technological internet and web architectures has led to the need for organizations to provide rental storage space through cloud of things. WoT integrates social computing, hyperconnectivity technologies and internet platform to provide communication with WWW that leverages data, things, people and process for planning and decision-making. In the competitive business environment, information organizations are adopting technologies that offer the prospects of value adding and enriching of academic products and services through web ranking, e-marketing and e-branding strategies including online, web, internet or cloud-based solutions (Shukla et al., 2012). Digital content system has impacted on university education and training by providing platforms for scholarly dissemination and visibility of information resources. Web and digital repositories have become the core resources for publishing quality academic and research papers and articles that reflect remarkable openness and excellence rankings globally for local content that were initially underutilized and untraceable. Digital institutional repositories provide the platform for open access and dissemination of scholarly research output in academic institutions (Bhardwaj, 2014; Boufarss, 2010). This allows sharing and exchange of
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information and knowledge through search engines and open-access aggregators and databases, provides value-added services to the content (Knoth and Zdrahal, 2012) and enables users to discover resources using web-based tools as opposed to traditional ones (Falciani-White, 2012). Hyperconnectivity, social media and communication Social media are used in a variety of communication methods through broadcast messages, response to enquiries and online conversation with the clients. In the current business world, marketers and businesses are rushing to online social media sites because that is where customers, suppliers, prospective employees and other stakeholders are to be found (Rupak et al., 2014). Social media platforms (Facebook, Twitter, blogs and LinkedIn) have demonstrated excellent opportunities that promote delivery of information resources and services while fostering innovative and excellent communication opportunities. Similarly, social media tools available via the IoT provide effective tools and services to share and disseminate information by interactively collaborating with each other in digital communities through blogs, social networking and video sharing sites (Farid, 2013). The fundamental mandate of information work is to share content and knowledge for societal and individual development and growth. IoT also offers communication technologies for hyperconnectivity and networking purposes through electronic devices, mobiles, information facilities and wireless sensors (Wi-Fi and wireless mesh networks). In business and information establishments, the focus to quality services through connectivity to the power of social media and increased consumer demand for digital and mobile computing devices makes IoT a necessary requirement in organizations. Hyperconnectivity and networking technologies have made remote access possible for huge databases of big libraries in developed countries to be used for the purpose of adopting, adapting and sharing of bibliographic data and information (Adeleke and Olorunsola, 2010). The magnitude and multiplicity nature of information organizations have undergone fundamental changes with diverse implications to the institutions and the customers. This connects students with information and knowledge using digital systems and solutions such as e-learning, e-information and m-learning practices anywhere anytime. IoT is about 6A connectivity – anything, anytime, anyone, any place, any service and any network (Vermesan et al., 2011). Digital information and knowledge require dedicated and powerful technologies that provide unlimited opportunities through devices that depend upon connectivity and communication whenever wherever. Quality information infrastructure support is necessary in supporting IoT technologies and hyperconnectivity. Virtual and remote services and help desks In the IoT environment, information organizations have the virtual power to solve client queries through smart technological systems such as online help desk, remote services and robotic systems. Mobile devices and smartphone technologies are used to monitor, control and promote information services to the clients through video, social media connections, online discussions and digital content. As more devices become connected, campus leaders will be able to extract much more value from the continuous stream of data and information, helping them move from a transactional relationship with students, faculty, administrators and providers to an iterative process in which minor decisions can be made on an ongoing basis (Asseo et al., 2016). With the IoT, virtual reference information services are offered through platforms of email, instant messaging and chat discussions anywhere anytime in any connection. Chatbot is a 24-h virtual reference service that responds to the clients without the intervention of human resource wherein the customers ask question in web-
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based environment and robot answers the question both in voice and text. This enables hyperconnectivity and networking with information professionals and clients across the globe. PocketLab and Lab4U are IoT sensor solutions, researchers use through smartphones that provide powerful, but low-cost, science lab instruments capable of measuring acceleration, force, angular velocity, pressure, magnetic field, temperature and altitude. When combined with robotics and controllers, the sensors enable students to run, monitor and directly participate in science experiments online (Nilsson, 2016). Penetration of internet of things in information organizations Penetration of IoT technologies in academic and research information organizations is a potential challenge due to significant investment involved in terms of money, manpower and time (Pujar and Satyanarayana, 2015) and variety of data collection and reporting methods needed and used (Jones, 2015). Protecting intelligent information technologies and systems in the IoT from multiple threats of data leakage and external networks is a taunting task (Li et al., 2015). Data and information security and privacy are two most challenging factors in IoT that affect personal functions, business processes and procedures, information, transport and communication (Li et al., 2015). Risks and perils connected with privacy, loss of data, entrusting too much control to technology and technical devices and legal problems affect IoT (Yang et al., 2011). Information security requirements and measures worth noting are confidentiality, access control, encapsulation, encryption, signature and authentication (Sicari et al., 2014) and virtual private network (extranet used by members of a group) (Weber, 2010). Information privacy is one of the most sensitive themes for IoT and legal protection mechanism design should be put into consideration (Li et al., 2015). Rapid growth of IoT outstrips economic and social demands, leading to the need for relevant communication and information regulations and policy especially for data protection privacy and intellectual property rights to help curb challenges (OECD, 2012). In the digital economies and societies, opinion formers, technologists and policymakers need to appreciate the policy and social challenges intertwining the usage of IoT. If the right policies, practices and business models are established and adopted, IoT is expected to fuel major economic, social and service innovations in present and future years (Löffler and Tschiesner, 2013). Research methodology Research design and sample and sampling techniques Quantitative research design applying structured questionnaire was purely the main method for collection of data and information from the participants. Academic and research information organizations in public and private chartered institutions were adopted in the research. In addition, the study used content and information analysis from electronic journals, empirical studies and websites. Information from websites, weblogs, webometrics ranking and social media interfaces from the academic and research information organizations were equally used. Research questions addressed various aspects and elements of the objectives of the study as outlined in the questionnaire under various themes and subthemes – IoT and transformation in information organizations, range of IoT technologies, technologies influencing application of IoT and strategies for effective management of IoT. Sample size for the research included 100 respondents – information professionals, systems librarians and technologists purposely selected from academic and research information organizations. Notably, all the respondents who participated in the research returned the questionnaires. From technological developments and perspectives, the selected respondents were involved in duties and responsibilities of information and
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Figure 1. Factors responsible for development of internet of things
communication technology, data and information management, digital content management, internet and electronic information and web and media development. Data were analyzed and the information presented in percentages, tables and figures. Discussion of findings First objective was to examine the potential application of IoT in transformation of academic and research information organizations. The objective highlighted a number of issues and concerns such as factors that promote the development of the IoT, potential capabilities and opportunities of the IoT and significance of the innovation in the modern knowledge and digital economy. Factors responsible for the development of the IoT in the contemporary knowledge economy and society were expressed as indicated in Figure 1. Question with multiple answer options that sought to determine the potential capabilities and opportunities of the IoT in academic and research information organizations established only two aspects of the answer – excellent or good ranking. Responses ranked “excellent” with 95 per cent and above are fountain of knowledge (97 per cent), unlimited accessibility (96 per cent) and online information (95 per cent), while the rest are rated “good” as indicated in Table I. The study also sought to determine the significance of the IoT in promoting academic and research information organizations based on positive and negative perceptions as follows: (1) Positive perceptions: capability for information and knowledge; reinvents information organizations; provides digital and internet information; fosters innovation and creativity; data and information for planning and decision-making; supports research, teaching and learning; promotes information marketing and branding; promotes non-academic functions – finance, medical; enhances green information technology practices; enables green information movement; expands education, information and communication space; and promotes green physical infrastructure.
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Potential capabilities and opportunities Fountain of knowledge Online information Information automation Information portal Education and research Data and information analytics E-learning support Unlimited accessibility Communication platform Online discussion Political, economic and social information General information
(2)
Excellent 97 (97) 95 (95) 80 (80) 88 (88) 90 (90) 20 (20) 17 (17) 96 (96) 91 (91) 90 (90) 21 (21) 20 (20)
Respondents number and % Good Fair Poor 3 (3) 5 (5) 20 (20) 12 (12) 20 (20) 80 (80) 83 (83) 4 (4) 9 (9) 10 (10) 79 (79) 80 (80)
– – – – – – – – – – – –
– – – – – – – – – – – –
Total 100 (100) 100 (100) 100 (100) 100 (100) 100 (100) 100 (100) 100 (100) 100 (100) 100 (100) 100 (100) 100 (100) 100 (100)
Negative perceptions: needs massive financial resources; information security and control; hacking and virus issues; and information infrastructure.
Notable positive perceptions of significance include capability for information and knowledge, reinvents information organizations, provides digital and internet information, fosters innovation and creativity, data and information for planning and decision-making and supports research, teaching and learning, while negative ones are needs massive financial resources, concerns of information security and control, hacking and virus issues and information infrastructure. Findings confirm that IoT has indeed transformed and changed the contemporary information and communication environment in academic and research organizations where access to data, information and knowledge is through application of personal systems (mobile devices) and digital information systems (Wordofa, 2014; Lwoga, 2014; Eden, 2012; Ozuorcun and Tabak, 2012). Potential capabilities of the IoT are best exemplified through innovative education and information practices such as e-learning, mobile learning and online information. Quality of services in academic and research information organizations is a fundamental aspect of technological systems and solutions in the modern digital environment. In the contemporary sustainable development practice, academic and research organizations have to provide robust technological solutions so as to attain the educational and informational needs of the customers. Second objective was to explore the range of IoT technologies applied to heighten access to knowledge and learning in academic and research information organizations. Asked to mention the range of IoT technologies applied in academic and research information organizations, majority of the respondents selected either strongly important or very important. Full 97 per cent of respondents selected digital or electronic information, with 70 per cent turning to digital information systems in the strongly important category, while data analytics and dashboard, multimedia applications and enterprise information systems are fundamentally highlighted as very important technologies as shown in Table II. Participants also indicated leading IoT technologies used to heighten access to knowledge
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Table II. Range of internet of things technologies
Internet of things technologies Data analytics and dashboard Mobile platform Cloud computing E-learning RFID Digital or electronic information Social media Hyperconnectivity Social computing Wearable or bring your own device Web of things Enterprise information systems Multimedia applications Digital information systems
SI 90 (90) 90 (90) 80 (80) 95 (95) 97 (97) 96 (96) 89 (89) 75 (75) 90 (90) 88 (88) – – 70 (70)
Respondents number and % VI IM SI 80 (80) – – – – – – – – – – 60 (60) 70 (70) –
– – – – – – – – – – – – – –
– – – – – – – – – – – – – –
NI
DK
– – – – – – – – – – – – – –
– – – – – – – – – – – – – –
Notes: SI = strongly important; VI = very important; IM = mportant; SI = somewhat important; NI = not important; DK = don’t know
and learning in academic and research information organizations with WoT on top as shown in Figure 2. Research results correlate with the literature studies to indicate massive application and use of IoT technologies in academic and research organizations in enterprise information management systems (Makori and Osebe, 2016; Dempsey et al., 2014), data and information analytics (Bradley et al., 2013; Dawson et al., 2013) and information intelligence systems (Lakshmi et al., 2014), cloud of things (Romero, 2012) and mobile of things (Johnson et al., 2010). In addition, there is evidence of wide spread use of technological systems and solutions in academic and research information organizations including RFID and automation of information (Pujar and Satyanarayana, 2015). With massive explosion of big data, there is tremendous use of analytics, visualization and dashboard in providing information necessary to support planning and decision-making in organizations through personal devices such as smartphones and laptops (Wordofa, 2014; Lwoga, 2014; Hashemi et al., 2011; Eden, 2012).
Figure 2. Leading internet of things technologies
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Third objective was to determine the technologies influencing the application of IoT in academic and research information organizations. Question with multiple responses was used to select the appropriate and relevant technologies influencing the application of IoT in information organizations. Admittedly, internet, web, hyperconnectivity and networking and cloud computing are the biggest influencers behind the application of IoT in academic and research information organizations as explained in Figure 3. Numerous technological innovations have been connected and associated with the rapid growth and use of IoT as exemplified through development of the internet platform (Internet Society, 2015; Rifkin, 2014; Groupe Speciale Mobile Association, 2014), WoT (Dawson et al., 2013), digital information (Davis et al., 2012; Eden, 2012; Ozuorcun and Tabak, 2012), hyperconnectivity and connectivity (Vermesan et al., 2011), automation of information (Molnár et al., 2013; Makori and Osebe, 2016), mobile smart applications (Johnson et al., 2010), cloud computing (Li et al., 2015) and social media (Rupak et al., 2014). Contemporary information and knowledge environment in academic organizations is full of digital and technological gadgets. The single biggest influencer and promoter of innovative technological systems and solutions in the modern digital economy is the IoT. IoT technologies promote access and use of data, information and knowledge anywhere anytime provided one is connected and networked. Fourth objective was to investigate practical strategies for enhancing effective management of IoT in academic and research information organizations. Participants were requested to highlight practical strategies including fundamental and priority institutional requirements to enhance sustainable IoT in academic and research information organizations. Academic and research organizations need to invest heavily in technological systems and solutions if quality services to the customers is to be attained in the sustainable information environment. Strategies for managing IoT that academic and research organizations have to offer are as shown in Figure 4. Reportedly, the respondents suggested particular fundamental and priority things that academic and research information organizations need to offer in the IoT environment including technology investment (28 per cent), leadership and management (27 per cent), digital information systems (24 per cent) and green movement (21 per cent) in Figure 5. Findings relate to studies conducted in stressing the need for organizations to put in place strategies and robust leadership programs and practices for promoting IoT and associated technologies to facilitate data and
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Figure 3. Technologies influencing application of internet of things
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information management for planning and decision-making (Jones, 2015; Li et al., 2015; Pujar and Satyanarayana, 2015; Löffer and Tschiesner, 2013; Zucca, 2013). Information organizations must adopt leadership programs based on demonstrated potentials and interests, transformation and change and return on investment for customers to have quality and value-added services.
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Conclusion IoT has significantly transformed and changed the nature of data, information and knowledge management in academic and research organizations with innovative technological systems and solutions that have expanded education, learning and competition. Power of IoE has proved to be favorable in academic and research information organizations, and therefore, technology may no longer be regarded as a factor hindering access to knowledge. Modern and emerging aspects of IoT environment responsible for sustainable knowledge, research and learning must be supported and promoted in academic and information organizations. Return on investment and massive application of resources are fundamental elements in development and utilization of IoT technologies in organizations. Massive data and information resources are produced, and therefore, powerful business tools and insights for harvesting and
Figure 4. Strategies for managing internet of things
Figure 5. Fundamental and priority institutional requirements
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mining relevant information for strategic planning and decision-making purposes are necessary. Fundamental aspects that need attention in organizations to foster innovation of IoT include hyperconnectivity and information infrastructure. Academic and research information organizations need to provide adequate financial resources so as to support and maintain connectivity whenever and wherever – at all times. Academic and research information organizations have economic challenges that can be supplemented through deployment of appropriate IoT technologies. Customers can make use of own devices and mobile smart applications in academic and research information organizations where it is not practically possible to provide enough social computing and technological needs.
Recommendations Academic and research information organizations should champion the deployment of IoT technologies to achieve sustainable research and learning goals and practices. IoT brings together four elements of strategic planning and decisionmaking – data and information; people; technology; and processes. Collaboration and partnership initiatives can help to bring academic institutions and information organizations together so as to promote innovation and creativity. Equally, through research and development, academic and research information organizations can engage in technological and digital initiatives to expand education, information, knowledge and communication. Technological development and information infrastructure in academic and research information organizations must be supported through robust management practices and massive utilization of resources. Comprehensive, security and control practices must also be put in place to ensure protection of data and information resources. Organizations and stakeholders need to recognize the power and insights of IoT that has provided the platforms to access content anywhere anytime in any technological device. Massive transformation and emerging trends in the digital and internet environments are fundamental sustainable development practices that enhance information change and innovation in organizations.
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Bradley, J., Barbier, J. and Handler, D. (2013), “Embracing the internet of everything to capture your share of $14.4 trillion”, White Paper, Cisco Systems, San Jose, CA. Chinyao, L., Yahsueh, C. and Mingchang, W. (2011), “Understanding the determinants of cloud computing adoption”, Industrial Management & Data Systems, Vol. 111 No. 7, pp. 1006-1023. Davis, C.H.F. III., Deil-Amen, R. Rios-Aguilar, C. and González Canché, M.S. (2012), “Social media and higher education: a literature review and research directions”, Report printed by the University of Arizona and Claremont Graduate University, available at: https://works.bepress.com/hfdavis/2/ Dawson, M.E., Jr, Crespo, M. and Brewster, S. (2013), “DoD cyber technology policies to secure automated information systems”, International Journal of Business Continuity and Risk Management, Vol. 4 No. 1, pp. 1-22. Dempsey, L., Malpas, C. and Lavoie, B. (2014), “Collection directions: some reflections on the future of library collections and collecting”, Portal: Libraries and the Academy, Vol. 14 No. 3, available at: www.oclc.org/content/dam/research/publications/library/2014/oclcresearch-collection-directionspreprint-2014.pdf Dimitrios, M., Dimitrios, C. and Charalampos, T. (2011), “Factors affecting ERP system implementation effectiveness”, Journal of Enterprise Information Management, Vol. 25 No. 1, pp. 60-78. Dzidonu, C. (2010), “An analysis of the role of ICTs to achieving the MDGs: a background paper”, Paper Commissioned by the Division for Public Administration and Development Management of the United Nations Department of Economic and Social Affairs, available at: http://unpan1.un.org/ intradoc/groups/public/documents/un-dpadm/unpan039075.pdf Eden, D. (2012), ECAR Study of Undergraduate Students and Information Technology, EDUCAUSE Center for Applied Research, available at: http://net.educause.edu/ir/library/pdf/ss15/ers1510ss. pdf El-Hussein, M.O.M. and Cronje, J.C. (2010), “Defining mobile learning in the higher education landscape”, Educational Technology & Society, Vol. 13 No. 3, pp. 12-21. European Union (2010), “Vision and challenges for realising the internet of things”, Cluster of European Research Projects on the Internet of Things (CERP-IoT), Information Society and Media, available at: https://books.google.co.ke/books/about/Vision_and_Challenges_for_Realising_the. html?id=nIRkMwEACAAJ&redir_esc=y Falciani-White, N. (2012), Understanding Information Seeking Behavior of Faculty and Students: A Review of the Literature, Wheaton College, Wheaton, available at: www.igi-global.com/chapter/ understanding-information-seeking-behavior-faculty/67811 Farid, S. (2013), “Social media and the social movements in the middle east and North Africa”, Information Technology & People, Vol. 26 No. 1, pp. 28-49. Fei, T., Ying, Z., Li, X. and Lin, Z. (2014), “IoT-based intelligent perception and access of manufacturing resource toward cloud manufacturing”, IEEE Transactions on Industrial Informatics, Vol. 10 No. 2, pp. 1547-1557. Frenzel, L. (2012), “A successful internet of things hinges on M2M”, available at: http://electronicdesign. com/iot/successful-internet-things-hinges-m2m Groupe Speciale Mobile Association (2014), “Understanding the internet of things”, available at: www. gsma.com/connectedliving/wp-content/uploads/2014/08/cl_iot_wp_07_14.pdf Gubbi, J., Buyya, R., Marusic, S. and Palaniswami, M. (2013), “Internet of things (IoT): a vision, architectural elements and future directions”, Future Generation Computer Systems, Vol. 29 No. 7, pp. 1645-1660. Hashemi, M., Azizinezhad, M., Najafi, V. and Nesari, A.J. (2011), “What is mobile learning? Challenges and capabilities”, Procedia Social and Science Behaviour, Vol. 30, pp. 2477-2481. Internet Society (2015), “The internet of things: an overview and understanding the issues and challenges of a more connected world”, available at: www.internetsociety.org/doc/iot-overview
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Johnson, L., Levine, A., Smith, R. and Stone, S. (2010), The 2010 Horizon Report, The New Media Consortium, Austin, TX. Jones, G. (2015), “The rise of the machines: now what?”, available at: www.wired.com/insights/2015/02/ the-rise-of-the-machines-now-what/ Knoth, P. and Zdrahal, Z. (2012), “Core: three access levels to underpin open access”, D-Lib Magazine, Vol. 18 No. 11-12, available at: www.dlib.org/dlib/november12/knoth/11knoth.html Kulkarni, P. and Prachi, J. (2015), Artificial Intelligence: Building Intelligent Systems, PHI Learning Pvt. Ltd, available at: https://books.google.co.in/books/about/ARTIFICIAL_INTELLIGENCE.html? id=JwW-CAAAQBAJ Lakshmi, P., Chandra, P., Alexander, W., Suri, R. (2014), Robotics a Project-Based Approach, Cengage Learning Trade, available at: www.amazon.com/Robotics-Project-Based-Approach-LakshmiPrayaga/dp/1305271025 Li, S., Xu, L. and Zhao, S. (2015), “The internet of things: a survey”, Information Information Systems Frontiers, Vol. 17 No. 2, pp. 243-259. Löffler, M., Tschiesner, A. (2013), The Internet of Things and the Future of Manufacturing, McKinsey and Company, available at: www.mckinsey.com/business-functions/digital-mckinsey/ourinsights/the-internet-of-things-and-the-future-of-manufacturing Lueth, K.L. (2015), “The 10 most popular internet of things applications right now”, available at: https:// iot-analytics.com/10-internet-of-things-applications Lwoga, E.T. (2014), “Integrating web 2.0 into an academic library in Tanzania”, Electronic Library, Vol. 32 No. 2, pp. 183-202. Makori, E.O. and Osebe, R.M. (2016), “Koha enterprise resource planning system and its potential impact on information management organizations”, Library Hi Tech News, Vol. 33 No. 4, pp. 17-23. Molnár, B., Szabó, G. and Benczúr, A. (2013), “Selection process of ERP systems”, Business Systems Research, Vol. 4 No. 1, pp. 36-48. Morgan, J. (2014), “A simple explanation of ‘the internet of things’”, Forbes/Leadership, available at: www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyonecan-understand Morton-Owens, E. and Hanson, K.L. (2012), “Trends at a glance: a management dashboard of library statistics”, Information Technology and Libraries, Vol. 31 No. 3, pp. 36-51. Murphy, S.A. (2013), “Data visualization and rapid analytics: applying tableau desktop to support library decision-making”, Journal of Web Librarianship, Vol. 7 No. 4, pp. 465-476. Nilsson, B. (2016), Will There Still Be a Classroom in 2020? – How Did a Dinosaur Get in the Room?, Extreme Networks, available at: http://content.extremenetworks.com/h/i/319064664-will-therestill-be-a-classroom-in-2020-how-did-a-dinosaur-get-in-the-room OCLC (2014), “Libraries and the internet of things”, American Libraries Magazine, available at: https:// americanlibrariesmagazine.org/blogs/the-scoop/libraries-and-the-internet-of-things ODLIS (2016), “Collection management”, available at: www.slideshare.net/PAARLOnline/datadrivencollection-management OECD (2012), “Machine-to-machine communications: connecting billions of devices”, OECD Digital Economy Papers, No. 192, doi: 10.1787/5k9gsh2gp043-en. Ozuorcun, N.C. and Tabak, F. (2012), “Is m-learning versus e-learning or are they supporting each other”, Procedia Social and Science Behaviour, Vol. 46, pp. 294-305. Piyawan, S., Kulthida, T. and Cholabhat, V. (2011), “Factors affecting customer relationship management practices in thai academic libraries”, The International Information & Library Review, Vol. 43, pp. 221-229. Pujar, S.M. and Satyanarayana, K.V. (2015), “Internet of things and libraries”, Annals of Library and Information Studies, Vol. 62, pp. 186-190.
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Richard, K. (2012), Building and Managing e-Book Collections: A How-to-Do-It Manual for Librarians, American Library Association, available at: www.alastore.ala.org/detail.aspx?ID=3979 Rifkin, J. (2014), The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons and the Eclipse of Capitalism, Macmillan, New York, NY. Romero, N. (2012), “Cloud computing in library automation: benefits and drawbacks”, The Bottom Line, Vol. 25 No. 3, pp. 110-114. Rupak, R., Greg, R., Jei, Y. and Ben, J. (2014), “Technology acceptance model (TAM) and social media usage: an empirical study on facebook”, Journal of Enterprise Information Management, Vol. 27 No. 1, pp. 6-30. Shukla, S., Agarwal, S. and Shukla, A. (2012), “Trends in cloud-ERP for SMB’s: a review”, International Journal of New Innovations in Engineering and Technology, Vol. 1 No. 1, available at: https:// pdfs.semanticscholar.org/7496/b5631f1a1c9aa729ff32df2f566cfaab1503.pdf Sicari, S., Rizzardi, A., Grieco, L. and Coen-Posimi (2014), “Security, piracy and trust in internet of things: the road ahead”, Computer Networks, Vol. 76, pp. 146-164. Srinivasa, R.N. and Brahmaiah, G. (2016), “Mobile library services and technologies: a study”, International Journal of Research in Library Science, Vol. 2 No. 2, pp. 59-66. Vermesan, O., Friess, P., Guillemin, G.S., Sundmaeker, H., Bassi, A. and Doody, P. (2011), “Internet of things strategic research roadmap”, Internet of Things: Global Technological and Societal Trends, Vol. 1, pp. 9-52. Vollmer, T. (2010), There’s an App for That! Libraries and Mobile Technology: An Introduction to Public Policy Considerations, American Library Association, New York, NY. Weber, R. (2010), “Internet of things: new security and privacy challenges”, Computer Law and Security Review, Vol. 26 No. 1, pp. 23-30. Wordofa, K.H. (2014), “Adoption of web 2.0 in academic libraries of top african universities”, Electronic Library, Vol. 32 No. 2, pp. 262-277. Yang, G., Geng, G., Du, J., Liu, Z. and Han, H. (2011), “Security threats and measures for the internet of thing”, Journal of Tsinghua University Science and Technology, Vol. 51 No. 10, pp. 1335-1340. Zucca, J. (2013), “Business intelligence infrastructure for academic libraries”, Evidence Based Library and Information Practice, Vol. 8 No. 2, pp. 172-182, doi: http://dx.doi.org/10.18438/B83G75. Further reading Kawatra, P.S. (2013), The Text Book of Information Science, PH Publishing House, New Delhi.
Appendix. Questionniare for respondents BACKGROUND INFORMATION 1. Name of informaon management instuon …………………………………….……………………………………….. 1. Public University 2. Private University 2. Profession/occupaon …………………………………….……………………………….............................................. 3. Informaon profession 4. Technology and compung INTERNET OF THINGS AND TRANSFORMATION OF INFORMATION ORGANIZATIONS
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3. Select the factors responsible for the development of the internet of things in the contemporary knowledge economy and society. Development of Internet of Things Internet development Technological innovaon Digital transformaon Compung revoluon Mobile revoluon Globalizaon Compeon Quality services Customer needs Research and development
Mulple Response Opons
4. Rank the potenal capabilies and opportunies of the internet of things in academic and research informaon organizaons. Mulple response answer, where 4=Excellent, 3=Good, 2=Fair, 1=Poor Potenal Capabilies and Opportunies Fountain of knowledge Online informaon Informaon automaon Informaon portal Educaon and research Data and informaon analycs E-learning support Unlimited accessible Communicaon plaorm Online discussion Polical, economic and social informaon General informaon
4
3
2
1
5. State the significance of the internet of things in promong academic and research informaon organizaons. Tick the appropriate opon. Significance Internet of Things Capability for informaon and knowledge Reinvents informaon organizaons Provides digital and internet informaon Fosters innovaon and creavity Data and informaon for planning and decision making Supports research, teaching and learning Promotes informaon markeng and branding Promotes non academic funcons – finance, medical Enhances green informaon technology pracces Enables green informaon movement Expands educaon, informaon and communicaon space Needs massive financial resources Informaon security and control Hacking and virus issues Informaon infrastructure Supports green physical infrastructure
Posive
Negave
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RANGE OF INTERNET OF THINGS TECHNOLOGIES 6. Menon the range of internet of things technologies applied in academic and research informaon organizaons. On a scale of 1-6, pick the appropriate opon where 6=Strongly Important, 5=Very Important, 4=Important, 3=Somewhat Important, 2=Not Important, 1=Don’t Know. Internet of Things Technologies Data analycs and dashboard Mobile plaorm Cloud compung E-learning RFID Digital or electronic informaon Social media Hyperconnecvity Social compung Wearable or bring your own device Web of things Enterprise informaon systems Mulmedia applicaons Digital informaon systems
6
5
4
3
2
1
7. Indicate the leading internet of things technologies used to heighten access to knowledge and learning in academic and research informaon organizaons from queson 6. TECHNOLOGIES INFLUENCING APPLICATION OF INTERNET OF THINGS 8. Select the technologies influencing and managing the applicaon of internet of things in academic and research informaon organizaons. Technologies Influencing Internet Web Digital informaon Social compung Hyperconnecvity and networking Automaon of informaon Mobile smart applicaons Cloud compung Social media
Pick All Applicable Opons
STRATEGIES FOR EFFECTIVE MANAGEMENT OF INTERNET OF THINGS 9. Indicate the praccal strategies useful in enhancing effecve management of internet of things in academic and research informaon organizaons. Strategies for Managing Internet of Things Provide adequate funding resources Develop informaon infrastructure Collaboraon and partnership Policy formulaons Modern buildings
Tick All That Apply
10. Menon the fundamental and priority instuonal requirements that academic and research informaon organizaons need to offer so as to achieve sustainable internet of things and development goals. Strategies for Managing Internet of Things Technology investment Digital iniaves Green informaon movement Leadership and management
Pick The Right Opons
Corresponding author Elisha Ondieki Makori can be contacted at: [email protected]
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PUBLIC LIBRARIES: ROLES IN BIG DATA
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Public libraries: roles in Big Data
Roles in Big Data
Ming Zhan and Gunilla Widén Department of Information Studies, Åbo Akademi, Turku, Finland
Abstract
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Purpose – The purpose of this paper is to explore the roles of public libraries in the context of Big Data. Design/methodology/approach – A mixed method approach was used and had two main data collection phases. A survey of public libraries was used to generate an overview of which professional roles connect public libraries with Big Data. Eight roles were identified, namely, educator, marketer, data organiser, data container, advocator, advisor, developer and organisation server. Semi-structured interviews with library directors and managers were then conducted to gain a deeper understanding of these roles and how they connect to the library’s overall functions. Findings – Results of the survey indicated that librarians lack a proper comprehension of and a pragmatic application of Big Data. Their opinions on the eight roles are slightly stronger than neutral. However, they do not demonstrate any strong agreement on these eight roles. In the interviews, the eight roles attained more clear support and are classified into two groups: service-oriented and system-oriented roles. Originality/value – As an emerging research field, Big Data is not widely discussed in the library context, especially in public libraries. Therefore, this study fills a research gap between public libraries and Big Data. In addition, Big Data in public libraries could be well managed and readily approached by citizens in undertaking such roles, which entails that public libraries will eventually benefit from the Big Data era.
133 Received 14 June 2016 Revised 30 March 2017 Accepted 20 May 2017
Keywords Big Data, Public libraries, Professional roles Paper type Research paper
Introduction Even though there is no accurate figure for the amount of data created daily, there is no doubt that we are living in a generation of data explosion. Data is defined as unprocessed information in information science (Hey, 2004), such as footfall, online browsing history and trip routes, which are automatically recorded every day. Accordingly, society is faced with, on the one hand, challenges in handling issues, such as privacy protection and data storage capabilities, and, on the other hand, opportunities that could and should be realised. As such, Big Data emerges concomitantly, which changes the way society adapts to manage and govern the data (Chen and Zhang, 2014). According to studies by Heidorn (2011) and Gordon-Murnane (2012), libraries are now reaching a data-richness condition as well, owing to their ease of access to the internet and the worldwide availability, affordability and applicability of digital devices, the increasing number of digital resource types and the advanced technology necessary for data collecting, recording, analysing and aggregating. In other words, libraries are heading towards a situation where Big Data is continuously important due to technological developments and the condition of data-richness. Therefore, the influence stemming from Big Data is obvious in the context of libraries. As a knowledge hub, public libraries undertake the role of supporting citizens in organising their personal information. As Big Data has been demonstrated to have positive effects on pragmatic processes, such as knowledge The authors acknowledge the financial support from Turku City and the help of Kalle Varila from Turku Main Library. In addition, they are grateful for all the participants in the survey and the interviews, and comments and suggestions received from scholars and librarians.
The Electronic Library Vol. 36 No. 1, 2018 pp. 133-145 © Emerald Publishing Limited 0264-0473 DOI 10.1108/EL-06-2016-0134
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generation (Fuchs et al., 2014), user behaviour forecasting (Xiang et al., 2015) and decisionmaking (Chen et al., 2014), the ways in which Big Data is integrated into the current library system and transformed into valuable operations are relevant for public libraries in developing their services. Hence the question: RQ1. What is the role of the public library in managing Big Data and bringing Big Data to citizens? To answer RQ1, empirical research was conducted. Improvements in library services are anticipated through the investigation of the roles and uses that public libraries could employ when using Big Data. Big Data in public libraries could be well managed and, therefore, readily approached by citizens. There are two justifications for studying Big Data in public libraries. Firstly, this study is one part of a two-year government funded project, Big Cities Meet Big Data, which concentrates on the ramifications of Big Data in the public sector. Public libraries, as an element of the public sector, were chosen as an area of study for the project. Secondly, as an emerging research field, Big Data has not been widely discussed in the context of libraries, most specifically public libraries. Therefore, there is a research gap on the subject of public libraries and Big Data. The paper is structured with a literature review of Big Data in libraries, followed by the methodology and research data. The results are presented with their implications and discussion. Conclusions are made and expectations for future studies are put forward. Literature review Big Data and the library Libraries are in the Big Data era, as indicated by the availability of various digital devices; diverse origins of data; the enhanced ability to collect, store and handle data; and the predisposition to use data for decision-making (Affelt, 2015; Gordon-Murnane, 2012). As such, the model of the library has begun to evolve from Library 2.0 (emphasising user participation) and Library 3.0 (facilitating the management of user-generated content) to Library 4.0 “where not only inference and research are available, but the system will analyse information by itself and discuss findings with users” (Noh, 2015, pp. 791-792). After reviewing the relevant literature, Noh further notes that in Library 4.0, the volume of data and services to be managed by future libraries will be massive. Therefore, future libraries can be called massive data libraries, where Big Data plays the main role. Furthermore, libraries will not only be influenced by the advent of Big Data but also fuel the development of Big Data (Wittmann and Reinhalter, 2014). Therefore, it is rational to investigate public libraries in the context of Big Data. What is Big Data? Currently, there is no consensus on a definition for Big Data in librarianship. Hoy (2014, pp. 321-322) referred to Big Data as “the idea that computers can gather trillions of pieces of information about billions of different things and find useful patterns in that information” after reviewing definitions generated from previous studies. Federer (2016, p. 36) defined Big Data as four Vs: volume, the scale of data; velocity, the speed at which data are created; variety, the type of data; and veracity, the reliability and integrity of data. Aiming to outline the essential features of Big Data, De Mauro et al. (2016) reviewed 1,437 conference papers and journal articles and concluded that technology, information, method and impact are four features of Big Data. They defined Big Data as an “information asset characterised by such a high volume, velocity and variety to require specific technology and analytical methods for its transformation into value” (p. 131). Although different aspects have been emphasized
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in defining Big Data, one universal point is that Big Data has increased technological transformation in the library, and such a transformation requires librarians and information professionals to take on new roles (Affelt, 2015; Gordon-Murnane, 2012; Hoy, 2014; Wittmann and Reinhalter, 2014). The influence of Big Data on library roles According to Gordon-Murnane (2012), Hoy (2014) and Wittmann and Reinhalter (2014), librarians should take on more data-specific roles as Big Data enhances the data services within libraries. Libraries are needed to organise data, provide access to internal and external data sets, authorise copyright on property issues and facilitate the processes for reusing data and training users. To fulfil these functions, the skills of librarians should be updated. For instance, indexing and abstracting skills could be incorporated with Big Data technologies to locate valuable resources within a larger data resource. Reference interviewing skills are also important to understand what a customer needs and, thus, help to provide the best Big Data solution (Affelt, 2015; Hoy, 2014). Affelt (2015) also highlights three roles for information professionals working with Big Data: curator who determines where and how to obtain it; data cleanser who removes erroneous and duplicated data; and data archive manager who builds and maintains data warehouses. Eventually, the role of libraries is expected to evolve and encompass new roles to be performed by librarians and information professionals in the context of Big Data. According to each of the studies done by Hoy (2014), Huwe (2014) and Teets and Goldner (2013), libraries are well positioned to work with Big Data. Teets and Goldner (2013) stated that libraries should take on the function of sharing vast amounts of library collection data to benefit a larger audience and establish systems to forecast user patterns by collecting Big Data. For example, a PhD student from a small European institution may be concentrating on a similar project to that of an American professor working in a major research organisation; Big Data may help recognise that similarity and help them combine resources. Big Data generates new roles, responsibilities and challenges for libraries. Shen and Varvel (2013) emphasized, that although the promise of Big Data might exist, challenges must be met by academic libraries. Hence, effective data management services are required. To develop practical ideas for data management services in academic libraries, a case study of Johns Hopkins University (JHU) was conducted. A service-model framework was established in JHU’s data management services, which includes three aspects: environmental responsiveness, socio–technical readiness and marketing and collaborations. The primary factors for success which were discussed were how to ensure services based on Big Data. One way is adoption, which refers to how much the service is used and how much data are used and reused. Another factor is acceptance, which means how the service is varied as well as how it is appreciated in general. Overall, it was concluded that various factors, such as an organisation’s financial situation, staffing abilities and organisational cultures, should all be considered when providing data management services based on Big Data. All these studies indicate that Big Data and libraries could be readily combined with each other. Nevertheless, Big Data is mainly discussed in the library field from a general point of view. Or, to put it another way, only a few studies have examined Big Data in specific kinds of libraries, such as public libraries. This motivates the current study to outline some roles that public libraries could and should undertake in the context of Big Data to better serve citizens, communities and organisations.
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Table I. Explanation of the eight roles
Data collection and methodology The purpose of the present study is to explore the roles of public libraries in the context of Big Data. A mixed method approach was used and had two main data collection phases. A survey of public libraries was used to generate an overview of which professional roles connect public libraries with Big Data. Then, semi-structured interviews with library directors and managers were conducted to gain a deeper understanding of these roles and how they connect to the library’s overall duties. An online survey was conducted during the first stage. The aim was to collect opinions from librarians so as to pinpoint their preferences regarding the different roles that could be undertaken by public libraries in the context of Big Data. As the combination of public libraries and Big Data is in its infancy, no mature questionnaires could be used or referred to, so a questionnaire was designed. To include suitable content in the questionnaire, three librarians from a university library were interviewed. University librarians were selected for this step of acquiring a more general view because university libraries collaborate with researchers who have often already worked with the challenges stemming from Big Data. Therefore, it was assumed that these experiences might provide additional interpretations of the concept and enrich the discovery of library roles. In the end, seven roles were identified, namely, educator, marketer, data organiser, data container, advocator, advisor and developer. As public libraries provide services not only to individuals but also to organisations, interpreted by Stejskal and Hajek (2015) as having roles regarding the providing of services for organisations as well as individuals, the additional role of organisation server was included in the survey. Thus, eight roles were identified and their findings are supported by numerous studies (Affelt, 2015; Federer, 2016; Gordon-Murnane, 2012; Hoy, 2014; Teets and Goldner, 2013; Wittmann and Reinhalter, 2014). On basis of these eight roles, a questionnaire was designed. This questionnaire was sent to professionals for reviewing before being officially sent out to ensure its efficacy and reliability. Table I includes an explanation of the eight roles identified. The questionnaire was delivered online. It was sent out on 27 January 2016. Respondents had until 10 March 2016 to complete the survey. Results were recorded automatically in a Web-based format. The URL of the survey was sent to the reference e-mail address of public libraries in Finland (available at the website www.libraries.fi). Eventually, 49 responses were successfully attained. One was duplicated and thus deleted. A total of 48 cases were analysed using SPSS 17.0. Semi-structured interviews were conducted with library managers and directors to gain a deeper comprehension of the feasibility and applicability of public libraries with respect to the eight roles. The interviewees were selected from big city libraries in Finland. The rationale was to choose city libraries that are pioneers in Finnish librarianship and have branch libraries in small towns in their region with densely populated suburbs. Therefore,
Role
Explanation
Marketer Educator Data organiser Data container Advocator Adviser Developer Organisation server
Making the Big Data concept and the use of Big Data known to citizens Helping citizens understand what Big Data is Cleansing and maintaining sets of Big Data Storing Big Data Supporting individuals in using Big Data Provide advice to solve personal issues from a Big Data point of view Using Big Data to develop current and new services Serving other organisations from a Big Data point of view
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the opinions of the leaders of such libraries should have a strong influence on forming ideas about Big Data and libraries, and represent a broad range of views. Eleven interviews were carried out between 26 October and 24 November November 2016. The average time for each interview was 30 min. All interviews were conducted in English and were recorded and transcribed manually. During the interview, the interviewees’ daily practices in relation to data, their attitudes towards the eight roles identified in the survey and their opinions on Big Data were discussed. Content analysis was used to analyse the interview transcripts.
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Results Results of the survey The survey is composed of three parts: demographic information, the perception of Big Data and the librarians’ attitudes towards the eight roles. There were twice as many females as males, and most people were aged between 26 and 40 years. More than half of the respondents had a master’s degree, and nearly 30 per cent of the representatives had a bachelor’s degree, together accounting for over 80 per cent of the respondents. More than 81 per cent of the respondents had worked in a public library for at least three years. The results also show that 46 per cent of the respondents work with “library loans/document delivery” and that 60 per cent of them perform more than one kind of job. The responses regarding degrees, length of employment and broad working areas imply that these librarians are sufficiently professional to provide their opinions on the topic. The perception of Big Data is designed to determine whether current librarians have practical experience with Big Data, thus making it possible to interpret how well they understand this concept. As shown in Figures 1 and 2, most of the respondent librarians had heard of Big Data as a concept. Nevertheless, more than half of them did not have hands-on experience of dealing with Big Data. Meanwhile, it is worth highlighting that the number of librarians with experience of Big Data is not much lower than that of those who have not. In addition, the perceptions of the respondents were implicitly evaluated. The characteristics of Big Data (volume, variety, velocity and value) (Huang et al., 2015) were expressed, respectively. The frequency (from 0 for never to 6 for always) of dealing with data that have such characteristics was measured. The more often they work with such data, the deeper their perception of Big Data is expected to be. In the end, the overall mean value was 2.84, which indicates that the frequency of librarians working with Big Data occurs just a few times a month. The means of the items concerning volume, velocity and value are far lower than that of the item concerning variety. The unbalanced working frequency regarding different data characteristics was easily understood based on the responses of the librarians.
Figure 1. Responses about familiarity with the Big Data concept
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The overall mean values of each role are all higher than four, which is the scale of “either agree or disagree”. It can be concluded that librarians do not disagree completely, especially regarding roles, such as marketer, educator, advocator, advisor and developer; thus, they tend to somewhat agree with adopting these roles. The role as organisation server received the lowest score, almost four. This could imply that the librarians do not have a clear opinion on this role. Regarding individual items, Item 17 (“The public library should establish data warehouses to store and preserve data generated from users or from projects incorporating other public sectors”) was given the lowest grade, a mean value slightly lower than four, which means the respondents slightly disagree with this statement. Item 20 (“The public library should give users tools or instructions rather than the actual result when users have trouble in managing personal information”) received the highest mean value in Table II, indicating that most librarians positively agree with this statement. Checked individually, the results are visualised in Figure 3. As presented in Figure 3, 42 per cent of the respondents agree with the proposed roles. Half of the respondents are in the middle, from neutral to slightly agreeing. Among them, eight people had a mean value higher than 4.5, which means they show a slight tendency to
Figure 2. Responses about experiences dealing with Big Data
Roles
Item
Mean value
Overall mean value
Marketer
I12 I13 I14 I15 I16 I18 I16 I17 I19 I20 I21 I22 I23 I24 I25 I26 I27
5.27 4.85 4.83 5.60 4.54 4.69 4.54 3.81 5.58 5.79 4.86 5.38 4.54 5.00 4.71 4.10 4.10
5.06
Educator Data organiser Data container Advocator
Table II. Result of the librarians’ opinions on different library roles
Advisor Developer Organisation server
5.22 4.62 4.64 5.33 4.96 4.86 4.10
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agree. Thus, the number of respondents agreeing is 28, nearly 60 per cent of all respondents. The lowest individual score was 2.8, which implies that this librarian disagrees with all these proposed roles to some extent, and the highest score was 6.8, which is very close to the maximum scale of the survey: 7. At the end of the survey, an open question was asked to see whether additional roles could be added. Nevertheless, no role was put forward and the librarians expressed their opinions on Big Data in these terms instead: “the concept of Big Data itself is rather vague”; and “not sure what Big Data is”. Such expressions echo the result of the librarians’ perception on Big Data and signify an insufficient comprehension of the benefits and challenges of Big Data for current librarianship. In conclusion, the result of the survey indicates that librarians lack a proper comprehension of and a pragmatic application of Big Data. Their opinions on the eight roles are slightly stronger than neutral. However, they do not demonstrate any strong agreement on these eight roles.
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Interview results The interview data were analysed using content analysis to systematically categorise the findings. The focus is on identifying the awareness and perceived possibilities of Big Data in public libraries and on the possible roles that librarians in public libraries can undertake in this context. Big Data is a new phenomenon in Finnish public libraries. Almost every interviewee mentioned that they were unfamiliar with Big Data because it is new and has not been widely discussed in their libraries: I think we are just at the beginning of Big Data; and [. . .] in our library, there are 200 people working and I would say that many of them even don’t know what Big Data is.
This, on one hand, explains the low perception of Big Data reflected by the findings of the survey owing to the infancy of Big Data in libraries. On the other hand, it can indicate that all the library directors are mainly unfamiliar with Big Data. Although not knowing the exact definition of Big Data, they readily related Big Data to a “huge amount of data” or “data generated on social media”, which falls under the definition of Big Data to some extent. Therefore, they hold positive opinions towards Big Data being applied in public libraries:
Figure 3. The overall mean value of the proposed roles
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I am very positive about it. I think using Big Data will give better tools to serve citizens, to show politicians what we are doing. There are lot of things where we can dig into when we learn Big Data. Well, there are risks as we told. But it could be a huge possibility to us to use that (Big Data).
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The interviewees consider Big Data to be an effective approach for understanding the requirements of library patrons. Furthermore, tools could be developed with Big Data to support decision-making processes. In addition, Big Data boosts data reusability. As discussed, data reusability means reusing the data not only inside the library but also other public data; for instance, the use of parking places around the library area – people might not go to the library very often if parking places around the library are hard to find. It suggests that libraries could develop services, such as drive-in collection points. This example demonstrates the significance of comprehending the needs of the whole of society. As suggested by half of the library managers and directors, it is salient for libraries to understand their society and their city before they start to provide services that use Big Data. However, their optimistic attitude towards Big Data is predicated on three preconditions. In their opinions, the three preconditions should be met; otherwise, it would be challenging for libraries to utilise Big Data. The conditions are as follows: sufficient finances to improve the current infrastructure and technically harness Big Data; sufficient personnel to exploit the values of Big Data; and authorisation and legislation regarding the protection of individual privacy. As individual records are stored in the library system, obtaining the permission of citizens to use their personal data is a challenge for libraries wishing to use Big Data. Furthermore, laws need be passed to provide public libraries with the legal right to explore personal data. Opinions about the roles examined in the survey. Owing to the lack of hands-on experience in handling Big Data, library directors and managers discussed their understandings of the eight roles relating to the projects that they have done. Even though not every role was realised in each library, these roles are agreed as covering the main scope of the responsibilities of a Finnish public library. However, libraries should consider their main responsibility when deciding which roles should be undertaken. Every interviewee mentioned that the role of storing data should be undertaken by the National Library of Finland or outsourced to commercial systems so that all the other public libraries could easily access the data. One responsibility of the National Library of Finland is to provide data services to all libraries (public or academic) in Finland. Therefore, it would be wise to select the National Library of Finland as the only storage centre for library Big Data. In addition, four directors and managers repute the idea that such a data storage role could be accomplished by library system vendors. Currently, the library system in northern Finland is provided and maintained by private companies. Millions of pieces of information and data are recorded on the system server. Therefore, it may be effective to outsource such a role to library system providers as they have more experience at storing data. The data organiser role is accepted by library directors and managers. As public libraries in each region might have their own development strategy, it might lead to various requirements for the data. For example, geographic data are currently analysed for mapping out user needs in different locations in several cities. Therefore, public libraries could extract
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different data from the data storage centre and establish their own data sets for further utilisation. They also need to act as a data organiser after establishing the data set. However, small scale libraries might not participate in this role in that they are led by the main library in their region. In the interviews, the role of educator, marketer, adviser and advocator is reflected by each library’s involvement in social media and the internet. For instance, three library managers mentioned that courses concerning social media are arranged in primary schools and in the homes of elderly people. During the course, the benefits and applications of social media are introduced. Additionally, citizens could ask for specific advice to manage their information needs with the help of social media. According to three directors, a Facebook home page, posters and face-to-face communication are used to market new developments in their libraries. Thus, the interviewees saw the possibility and significance of public libraries performing these roles with the advent of Big Data. However, the limited time and resources of librarians make it difficult for libraries to undertake all these four roles together. Phrases such as “no money” and “short of staff” were constantly mentioned during the interviews. Therefore, a combined role was put forward by two managers: that of facilitator: [. . .] the way we could cover educator or marketer, advocator, and adviser is if the libraries work as a facilitator [. . .] so the library again is the medium between the actual experts of Big Data [. . .] and provide the possibilities, the means to meet people, and help them meet the actual experts.
Regarding the roles of developer and organisation, there are not many experiences that can be used to reflect on Big Data. Nevertheless, library directors and managers are positive about these two roles. As mentioned, many technologies or new tools could be applied in the library field as they are of potentially great value for developing services. However, a lack of knowledge and experienced professionals is an obstacle. In addition, the interviewees are looking forward to collaborating with other organisations. They hope that certain public sector organisations and private companies would consider the public library a trustworthy place to which they might turn, as suggested by the following quote: “our library system should be able to give our data for private companies”. Thus, the public library could be expected to offer some library data sets to other organisations. These organisations could then utilise the data sets according to their own strategy to attain a specific result. The aim of such a process is to broaden the audience for library services from citizens to organisations. However, as all public libraries now lack staff, few librarians would have the time to make plans to put everything into practice. Discussion Big Data, as a term, is no longer totally new in libraries, which is confirmed by the fact that more than 75 per cent of the respondents in the survey had heard of Big Data, not to mention that nearly 46 per cent of them had had practical experience of handling Big Data. However, hearing about Big Data does not mean understanding it. This situation could be reflected by the unbalanced working frequency in managing data with Big Data characteristics, and this was apparent in the answers to the open question. As they do not usually handle data in great volume and rapidly increasing speed, and rarely create value from such amounts of data, this could indicate that they might have a limited understanding of Big Data. This, to some extent, could be attributed to the statement by library directors and managers that Big Data is a new phenomenon. However, according to the responses from the survey and statements in the interviews, the respondents’ unfamiliarity of Big Data tends to be on the conceptual level. That is, they are not familiar with the definition of Big Data. Nevertheless,
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they are capable of relating Big Data to relevant items; therefore, inferences on how it could be used can be made owing to the librarians’ reflections on similar practices.
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The natural match between public libraries and Big Data On the basis of the positive attitude of the library managers and directors and the results of the open question in the questionnaire, it can be concluded that there is a natural match between public libraries and Big Data. Respondents made statements, such as the following: Libraries should be at the front and centre in developing the use of Big Data [. . .]
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Libraries have a big and important role in dealing with Big Data [. . .] The library sits on Big Data so the subject is unavoidable [. . .]
All the statements and the feedback obtained during the interviews imply that using Big Data would be a natural process for public libraries. This viewpoint echoes previous studies (Hoy, 2014; Huwe, 2014; Noh, 2015; Wittmann and Reinhalter, 2014). A public library is not only a data generator but also a data container. Numerous data are recorded within a public library. As was discussed during the interviews, millions of collection data, library economic statistics, user information and borrowing histories are normally stored in a public library. In addition, digitalisation in public libraries is promoted by the Finnish Government, boosting the volume, variety and velocity of digital data recorded within them. Therefore, managing Big Data is not something a library plans to do; it could naturally be associated with the increase in data. However, there is no guarantee that this natural match will make it easier to manipulate Big Data in a public library. The challenges of Big Data management in other areas could also exist in librarianship. No appropriate designs for handling vast amounts of data and a lack of proper resources for analysing data are two major challenges (Kaisler et al., 2013), as mentioned in responses to the survey and during the interviews. Unbalanced resource allocation could also be an issue because small libraries would have more difficulty in analysing and using Big Data, which implies a long process before Big Data is used at a national level. The need to understand society is also required before Big Data can be applied more effectively and specifically. However, that brings challenges in knowing current society. Therefore, the natural match between Big Data and public libraries is naturally accompanied by big challenges. Service-oriented roles The analysis regarding library roles in the survey indicates that not all respondents completely agree with the proposed roles, partly owing to their poor understanding of Big Data. However, after considering the interview results, it is concluded that librarians tend to agree with service-oriented roles, which are educator, marketer, advocator, adviser, developer and organisation server. These roles were put forward to mainly help library users understand and utilise Big Data or benefit from Big Data. The realisation of serviceoriented roles could help answer one part of the research question: What roles should the public library undertake to bring Big Data to citizens? According to the survey results, librarians slightly agree with having the role of educator, marketer, advocator and adviser, and they see the opportunity for using Big Data for the benefits of users. In their answers to the survey’s open question, words such as “guidance” or “help individuals” were mentioned several times, which implies that the responsibility of libraries to help users in the generation of Big Data is shared by librarians as well. Moreover, the opinions put forward during the interviews highlight the inclination to use these four roles. All the library
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directors and managers consider these four roles indispensable for Big Data. In addition, they hold strong expectations about developing services from Big Data and communicating with other public sectors and private organisations through Big Data. To better interpret these roles, three perspectives were generated for further discussion. Educator and marketer: from the perspective of enlarging the audience for Big Data Public libraries should take on the role of educator – and, thus, help citizens understand what Big Data is – and marketer, to make the concept of Big Data and the use of Big Data familiar to citizens. With the help of these two roles, more citizens would become acquainted with Big Data. As pinpointed by Hoy (2014, p. 324), “librarians will need to help their patrons understand what Big Data can and cannot do, and how it can best be used to achieve their research goals”. Even though Hoy’s research mainly concentrated on academic universities, the essence of libraries acting as an educator is shared by public libraries. Thus, public libraries ought to work as an educator thinking of Big Data as an emerging science, and teach people how to utilise it for their own good. Public libraries should also act as professional marketers when promoting the opportunities and benefits of Big Data as it has great potential for being developed into new services within the library. Similar ideas are also mentioned in other studies (Gordon-Murnane, 2012; Wittmann and Reinhalter, 2014). Adviser, advocator and organisation server: from the perspective of the user For public libraries, users are individuals, communities and other organisations. To smooth the utilisation of Big Data from the user’s point of view, three roles are proposed that serve different users. Advocator and adviser are aimed at individuals and communities, and organisation server is intended to serve other organisations. As mentioned by Hoy (2014), libraries have no exemption from being active as a technology adopter. Two ideas are put forward for involving Big Data in the library: guiding users to use potential databases and helping researchers in data management, such as data sharing and archiving. Such guidance and help could also be given to other organisations; thus, these two ideas suitably support libraries acting as an advocator, adviser and organisation server. Developer: from the perspective of service improvement Public libraries should assume the role of developers who discover useful information from Big Data and transform it into services. As Xiang et al. (2015) demonstrated Big Data techniques provided new insights for hotels wishing to realise the requirements of guests. A public library can also develop some services to further realise a patron’s needs. For instance, public libraries can cooperate with health-care centres to promote knowledge concerning disease prevention by using Big Data analytics. In other words, to function as a service developer, public libraries can not only pay attention to Big Data within themselves but also consider resources outside the library because the amount of open data has increased. Methods for creating services in the current data-driven generation could be a major role for public libraries to undertake. It should be outlined that many resources are required to develop service-oriented roles. A lack of money, professionals or time would be obstacles to functioning in these roles. Therefore, the combined role of a facilitator was put forward to help the library realise these roles in an effective way. Being a facilitator integrates the functions of the four roles of educator, marketer, adviser and advocator, and considers libraries as a platform where external professionals can be introduced to citizens. Compared with these four roles, the idea of a facilitator is more consistent with the current situation in public libraries. As the
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available resources are too limited to accomplish all four roles, it would be wise to let other people help libraries accomplish this task. System-oriented roles Compared with service-oriented roles, the respondents tended to hold conservative opinions on system-oriented roles, which are data organiser (cleansing and maintaining big data sets) and data container (storing Big Data). They relate to the perspective of sustainable development, which emphasises the task of storing and archiving data for further use. System-oriented roles answer the other part of the research question: What roles should public libraries undertake to manage Big Data? These two roles are accepted by library directors and managers. However, the roles of being data organiser and data container are related to the responsibility and the size of a public library. It is suggested that the National Library of Finland should act as the only place for storing data for Finnish libraries and the main library of each region should organise data for branch libraries. Therefore, the respondents in small libraries tended not to agree on these two roles; however, those from the main library considered them necessary roles. Thus, the attitudes towards those roles are not clear in the survey, suggesting that the scale and responsibility of a public library could decide whether these two roles are undertaken. In addition, no matter which role a public library undertakes, legislation issues, such as copyright and the right to use personal data, should be solved in advance; otherwise, the willingness of libraries to use Big Data in practice will be diminished. Conclusion The aim of this study was to explore roles that should be undertaken by public libraries in the context of Big Data. According to the study, there is a natural match between Big Data and public libraries even though there are challenges. The proposed eight roles of educator, marketer, advocator, adviser, developer, organisation server, data container and data organiser are largely accepted by the librarians in the survey. These eight roles can be classified into two groups: service- and system-oriented roles. For service-oriented roles, the library’s resources are significant because the more sufficient resources a library has the easier it is to operate in service-oriented roles. If resources are insufficient, public libraries can act as a facilitator and employ external resources to provide services regarding Big Data. As for system-oriented roles, they are not necessary for every library, especially for small libraries or branch libraries, because a library’s responsibility and scale make the difference in system-oriented roles. This study focuses solely on public libraries in Finland. In future studies, public libraries in other countries should be examined. Furthermore, this study does not concentrate on the importance of each role. A study examining role-ranking based on significance could enrich the content of the present study and help attain more systematic findings. References Affelt, A. (2015), The Accidental Data Scientist: Big Data Applications and Opportunities for Librarians and Information Professionals, Information Today, Medford, NJ. Chen, C.L.P. and Zhang, C.Y. (2014), “Data-intensive applications, challenges, techniques and technologies: a survey on Big Data”, Information Sciences, Vol. 275, pp. 314-347. Chen, M., Mao, S. and Liu, Y. (2014), “Big Data: a survey”, Mobile Networks and Applications, Vol. 19 No. 2, pp. 171-209.
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De Mauro, A., Greco, M. and Grimaldi, M. (2016), “A formal definition of Big Data based on its essential features”, Library Review, Vol. 65 No. 3, pp. 122-135. Federer, L. (2016), “Research data management in the age of Big Data: roles and opportunities for librarians”, Information Services and Use, Vol. 36 Nos 1/2, pp. 35-43. Fuchs, M., Pken, H.W. and Lexhagen, M. (2014), “Big Data analytics for knowledge generation in tourism destinations: a case from Sweden”, Journal of Destination Marketing and Management, Vol. 3 No. 4, pp. 198-209. Gordon-Murnane, L. (2012), “Big Data: a big opportunity for librarians”, Online, Vol. 36 No. 5, pp. 30-34. Heidorn, P.B. (2011), “The emerging role of libraries in data curation and e-science”, Journal of Library Administration, Vol. 51 Nos 7/8, pp. 662-672. Hey, J. (2004), “The data, information, knowledge, wisdom chain: the metaphorical link”, Intergovernmental Oceanographic Commission, available at: www.dataschemata.com/uploads/ 7/4/8/7/7487334/dikwchain.pdf (accessed 4 March 2016). Hoy, M.B. (2014), “Big Data: an introduction for librarians”, Medical Reference Services Quarterly, Vol. 33 No. 3, pp. 320-326. Huang, T., LiangLan, L., Fanga, X., An, P., Min, J. and Wang, F. (2015), “Promises and challenges of Big Data computing in health sciences”, Special Issue on Computation, Business, and Health Science, Vol. 2 No. 1, pp. 2-11. Huwe, T.K. (2014), “Big Data and the library: a natural fit”, Computers in Libraries, Vol. 34 No. 2, pp. 17-18. Kaisler, S., Armour, F., Espinosa, J.A. and Al, E. (2013), “Big Data: issues and challenges moving forward”, in Croll, P.R. (Ed.), 46th Hawaii International Conference on System Science (HICSS), IEEE, pp. 995-1004. Noh, Y. (2015), “Imagining library 4.0: creating a model for future libraries”, The Journal of Academic Librarianship, Vol. 41 No. 6, pp. 786-797. Shen, Y. and Varvel, V.E. (2013), “Developing data management services at the Johns Hopkins University”, The Journal of Academic Librarianship, Vol. 39 No. 6, pp. 552-557. Stejskal, J. and Hajek, P. (2015), “Effectiveness of digital library services as a basis for decision-making in public organizations”, Library and Information Science Research, Vol. 37 No. 4, pp. 346-352. Teets, M. and Goldner, M. (2013), “Libraries’ role in curating and exposing Big Data”, Future Internet, Vol. 5 No. 3, pp. 429-438. Wittmann, R.J. and Reinhalter, L. (2014), “The library: Big Data’s boomtown”, The Serials Librarian, Vol. 67 No. 4, pp. 363-372. Xiang, Z., Schwartz, Z., Gerdes, J.H., Jr. and Uysal, M. (2015), “What can Big Data and text analytics tell us about hotel guest experience and satisfaction?”, International Journal of Hospitality Management, Vol. 44, pp. 120-130.
About the authors Ming Zhan is a Doctoral Candidate of the Department of Information Studies at Åbo Akademi, who is committed to exploring the possibilities of applying Big Data to public libraries. Ming Zhan is the corresponding author and can be contacted at: mzhan@abo.fi Gunilla Widén is a Professor of Information Studies at the Åbo Akademi University. Her research interests are knowledge management and information behaviour. She has lead several large research projects financed by Academy of Finland, and currently leads a project about the impact of information literacy in the digital workplace (2016-2020). She has published widely in her areas of expertise and been appointed expert in several evaluation committees.
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RISK ASSESSMENT OF DIGITAL LIBRARY INFORMATION SECURITY: A CASE STUDY
The Electronic Library Risk assessment of digital library information security: a case study Zhengbiao Han, Shuiqing Huang, Huan Li, Ni Ren,
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Article information: To cite this document: Zhengbiao Han, Shuiqing Huang, Huan Li, Ni Ren, (2016) "Risk assessment of digital library information security: a case study", The Electronic Library, Vol. 34 Issue: 3, pp.471-487, https:// doi.org/10.1108/EL-09-2014-0158 Permanent link to this document: https://doi.org/10.1108/EL-09-2014-0158 Downloaded on: 09 April 2019, At: 11:46 (PT) References: this document contains references to 23 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 2268 times since 2016*
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Risk assessment of digital library information security: a case study Zhengbiao Han, Shuiqing Huang and Huan Li College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China, and
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Ni Ren College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China and Institute of Agricultural Economics and Information, Jiangsu Academy of Agricultural Sciences, Nanjing, China
Digital library information security 471 Received 12 September 2014 Revised 4 January 2015 1 April 2015 5 June 2015 Accepted 21 June 2015
Abstract Purpose – This paper uses the GB/T20984-2007 multiplicative method to assess the information security risk of a typical digital library in compliance with the principle and thought of ISO 27000. The purpose of this paper is to testify the feasibility of this method and provide suggestions for improving information security of the digital library. Design/methodology/approach – This paper adopts convenience sampling to select respondents. The assessment of assets is through analyzing digital library-related business and function through a questionnaire which collects data to determine asset types and the importance of asset attributes. The five-point Likert scale questionnaire method is used to identify the threat possibility and its influence on the assets. The 12 respondents include directors and senior network technicians from the editorial department, comic library, children’s library, counseling department and the learning promotion centre. Three different Guttman scale questionnaires, tool testing and on-site inspection are combined to identify and assess vulnerabilities. There were different Guttman scale questionnaires for management personnel, technical personnel and general librarian. In all, 15 management librarians, 7 technical librarians and 72 ordinary librarians answered the vulnerability questionnaire. On-site inspection was conducted on the basis of 11 control domains of ISO 27002. Vulnerabilities were scanned using remote security evaluation system NSFOCUS. The scanning covered ten IP sections and a total of 81 hosts. Findings – Overall, 2,792 risk scores were obtained. Among them, 282 items (accounting for 10.1 per cent of the total) reached the high risk level; 2 (0.1 per cent) reached the very high risk level. High-risk items involved 26 threat types (accounting for 44.1 per cent of all threat types) and 13 vulnerability types (accounting for 22.1 per cent of all vulnerability types). The evaluation revealed that this digital library faces seven major hidden dangers in information security. The assessment results were well accepted by staff members of this digital library, which testified to the applicability of this method to a Chinese digital library. Research limitations/implications – This paper is only a case study of a typical Chinese digital library using a digital library information security assessment method. More case-based explorations are necessary to prove the feasibility of the assessing strategy proposed in this study. Originality/value – Based on the findings of recent literature, the authors found that very few researchers have made efforts to develop methods for calculating the indicators for digital library
The authors gratefully acknowledge the financial support for this study by the national social science fund project of China. Project number is 12ATQ001.
The Electronic Library Vol. 34 No. 3, 2016 pp. 471-487 © Emerald Group Publishing Limited 0264-0473 DOI 10.1108/EL-09-2014-0158
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information security risk assessment. On the basis of ISO 27000 and other related information security standards, this case study proposed an operable method of digital library information security risk assessment and used it to assess a the information security of a typical Chinese digital library. This study can offer insights for formulating a digital library information security risk assessment scale. Keywords Digital library, Risk assessment, Vulnerability, Information security, Threats Paper type Case study
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472 1. Introduction Traditional research on library security is mainly concerned with paper-based information resources security (Bello, 1998;Ajegbomogun, 2004;Holt, 2007) and information systems security (Balas, 2005). Now, it has moved beyond a focus on handling security risk technically to a stage where equal importance is attached to technology and management (Ismail and Zainab, 2013). Compared with traditional library, digital library faces greater security risks due to its high dependence on computer technology, network technology, data communication technology and other high-tech technology. Once problems in information security arise, the operation and the service of the whole digital library system are likely to be affected. It is worth noting that digital library is defined in different ways (Anderson, 1997; Lesk, 1997; James and Thong, 2002). In this research, digital library is restricted to the digital parts built on traditional library, and the digital library information security management refers to the management of the digital parts of traditional library. In preliminary investigation of domestic digital library information security, Huang (2011) indicated that all 30 digital libraries had experienced information security incidents. Among these digital libraries, six had experienced one incident, ten had experienced two and the rest had experienced three or more. However, the information security practice on a specific digital library has been rarely studied. As for digital library organization department, how does the digital library organization department identify rapidly and effectively core assets under potential threats? How do they deal with the threats effectively? How can the management formulate internal policies and documents to guarantee information security? These problems need to be addressed from the perspective of digital library information security risk assessment. 2. Literature review Literature concerning digital library information security risk assessment mainly deals with four issues, namely, traditional library information security, digital library information security, the standards and specifications of digital library information security management and the information security risk assessment method. Traditional library information security lays foundation for this study because digital library as a new concept develops from it. The following are some important studies on traditional library information security issues. Maidabino and Zainab (2011) based on a review of library security and security parameters developed a comprehensive instrument for library security management and assessment, as well as a five-factor house model. Balas (2005) analyzed the measures taken to guarantee library computer safety.Ismail and Zainab (2013) propose an library information system security evaluation model, which comprises five components: technological security foundation, information security policy, procedures and control, administrative tools, methods and awareness creation. Abioye and Rasaki (2013) investigated security
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challenges in university libraries in Southwest Nigeria, which used questionnaires and interviews to collect data. Fox (2006) and Gressel (2014) emphasized the need to protect the information of library system users. Bowers (2006) proposed that both hacker and government agencies are interested in collecting library users’ information. These studies are different from each other, but all agree that library information security problems involve technology, management, users and outside stakeholder (e.g. government agencies) and maintained that library information safety management assessment should be holistically analyzed. The traditional library information security provides a theoretical basis for this study; and the factors, methods and approaches concerning traditional library security can be adopted in the study of digital library information security. But little was known about research devoted to digital library information security, as early research mainly focused on technical issues. Adam et al. (2002) proposed a content-based authorization model for digital libraries. Kuzma (2010) analyzed the vulnerability in European library websites and its effects on user data protection. This research showed that librarians in charge of Web systems did not take appropriate measures to protect their online information systems. Following the tendency to attach equal emphasis to technology and management in the information security domain, Anday et al. (2012) reviewed the literature over the 2000-2010 period concerning information security issues ranging from infrastructure, digital content, users and standards and legal issues. They believed that both technology and management play a vital role in digital library security. For the sake of effective guidance in information security management, it is essential that governments or trade association define standards in digital library information security management. The most important information security management standards are derived from a series of ISO/IEC 27000 of British BS7799. ISO 27001 was specially formulated for risk assessment. ISO 27011 and ISO 27799 are proprietary standards for telecommunication and medical industry. ISO 27015 is proprietary standard for financial industry, which is being developed by ISO. The risk assessment definition of ISO 27000 was cited from ISO Guide 73:2002. Risk assessment includes risk analysis and risk evaluation. The former used information systematically to identify risk sources and estimate the risk, and the latter compares identified risks by given risk criteria so as to assess the risk severity level. In recognition of its security efforts, OCLC has met ISO 27001 security standards and has received registrations. OCLC’s ISO 27001 information security management system is aligned with ISO 9001:2000 certified quality processes (www.oclc.org/content/dam/oclc/policies/security/oclc informationsecuritywhitepaper.pdf). In addition, there is a large amount of literature on library information security risk assessments. Lopez (2003) proposes a physical security control planning framework aiming at the safety tasks of Library of Congress.Michalko et al. (2010) mainly examined the greatest risks to research libraries and which of these risks is susceptible to mitigation. The results yielded a shared perspective on a landscape of challenges to US research libraries. Myongho (2011) recommended a library safety guideline that ensures the safety of digital library collections, users and physical structures by combining management, technology and physical entities. The thoughts and principles of ISO 27000 maintain a certain degree of independence from the specific assessments methods, so ISO 27000 series standards do not specify which kind of risk assessment methods should be adopted. Appendix E of ISO 27005
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exemplifies three different assessment methods, namely, value matrix method, threat hierarchical method and risk binary method (ISO and IEC, 2011). Formulated in accordance with the thoughts and principles of ISO 27000, GB/T20984-2007 also proposes two risk assessments methods. Therefore, ISO 27000 series standards can provide technical basis and valuable experience for formulating digital library information security management standards. Based on previous research and complying with ISO 27000 ideas and principles, this current article adopts GB/T20984-2007 multiplicative method as digital library information security risk assessment method and used it to assess a Chinese public digital library. The purpose of this article is to prove the operability of this method and provide guidance for improving information safety of digital library. 3. Methodology 3.1 Risk assessment object The object of digital library information security risk assessment is a large-scale public library in Guangdong, also known as one of China’s excellent libraries. In 2007 and 2008, it, respectively, won the second session ministry innovation prize of People’s Republic of China and international innovation prize of American Library Association (www.cpcss. org/_d271541097.htm). The digital library of this public library has already integrated document lending, information consulting, training and academic research functions, which is typical and representative in China. To understand the information security risk status of the digital library, the administration of the library provided some active co-operations during the assessment process. Therefore, the assessment underwent smoothly and offered findings to advice on digital libraries of the same kind. 3.2 Risk assessment method ISO 27000 is a widely inclusive international standard. To generalize ISO 27000 series standards in China, Chinese National Bureau of Standards released GB/T20984-2007 in compliance with the principle of ISO 27000 and in light of actual situation of China. GB/T20984-2007 can be used to guide information security risk assessment, identify security correctly and solve information security issues in China. According to the regulation of ISO 27001, risk value is determined by three indicators, which are asset, threat and vulnerability. However, so far, there are no explicit rules in various standards on how to work out risk values on the basis of the three indicators. Appendix E of ISO 27005 exemplifies three different assessment methods, namely, value matrix method, threat hierarchical method and risk binary method (ISO and IEC, 2011). Formulated in accordance with the thoughts and principles of ISO 27000, Appendix A of GB/T20984-2007 also proposes two risk assessment methods, namely, matrix method and multiplicative method. To some degree, it is fair to say that ISO 27005 and GB/T20984-2007 recommend these methods. In previous research, we analyzed these methods and other risk assessment methods in everyday work and used them to assess information security of several digital libraries. It proves that GB/T20984-2007 multiplicative method is more suitable for digital libraries than others because 兹A ⫻ T in multiple multiplicative method restrains asset value and threat contribution to risk value, and it also increases vulnerability contribution to risk value. It is in consonance with the information security situation of digital library. In general, digital library assets are stable. Digital library has
weak control ability on external threats. Compared with threats, digital library managers are more capable of handling safety concerns of assets. Therefore, this paper uses the GB/T20984-2007 multiplicative method as the digital library information security risk assessment method. The method is shown in equation (1), where R represents the risk value, T refers to the threat level of assets and V means the vulnerability level of assets:
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R ⫽ R ( A, T, V ) ⫽ 兹A ⫻ T ⫻ V
(1)
ISO 27000 series standards and GB/T20984-2007 make no provision for assessment of assets, threat and vulnerability, but GB/T20984-2007 proposes that assets, threat, vulnerability and risk level can be assigned by five levels in its body text. Based on the definition of assets, threat and vulnerability in GB/T20984-2007 and risk assessment of telecommunication industry information security (Fan, 2009), this case study puts forward equations (2), (3) and (4) to assess assets, threat and vulnerability. These formulas were used in several digital library information security risk assessments, and they have been proven effective. Assets are defined as resources that are owned or controlled by digital libraries and that bring social and economic benefits to digital library. Assets assessment takes into account three aspects, that is, the integrity (ai), the confidentiality (ac) and the availability (aa), as shown in equation (2): 〈⫽
ai ⫹ ac ⫹ aa 3
(2)
When vulnerability exists in digital libraries and security measures are absent, threat acts on assets in a certain way, causing damage and posing information security risk. Suppose Aj related threat is represented by T(Aj), the possibility is Tm, the extent to which integrity, confidentiality and availability of assets are affected are, respectively, represented by Ti, Tc and Ta, then the equation for threats calculating could be expressed as follows:
T(Aj ) ⫽
冪
Tm ⫻
Ti ⫹ Tc ⫹ Ta 3
(3)
Vulnerability refers to the weaknesses likely to be taken advantage of by the threat in assets. There is a many-to-many relationship between vulnerability and threat. In other words, vulnerability may be exploited by multiple threats and a threat may also relate to multiple vulnerabilities. Assume that V represents vulnerability, Vi represents the way i to detect vulnerability and n represents the number of ways to detect each type of vulnerability. The equation for vulnerability calculation is shown as equation (4):
V⫽
1 n
n
兺v i⫽1
i
(4)
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3.3 Index assignment and collection methods We start assets recognition with the businesses and functions of digital library. Having analyzed the assets related to those businesses, we classify and enumerate them to make a list of sorted assets. Then, based on data collected through questionnaires, we define the asset types and the importance of asset attributes. See Huang (2011) for detailed description of assets attributing methods and collecting methods. This paper focuses on the attributing methods and collecting methods concerning threats and vulnerability. 3.3.1 Threat index assignment and collection methods. With reference to the threat classification in “Information Security Risk Assessment Standards for Information Security Technology” of GB/T20984-20984, we take into consideration the actuality of the digital library assets and advice from three experts and, finally, select 52 threats. These threats can be classified into four types, namely, system (10 items), environment (8 items), nature (4 items) and personnel (30 items). These experts are curator, department head and technical librarian of public libraries. They have an intimate knowledge with the potential threats to public digital library. Questionnaires are adopted to control threat data in this study. Considering the actual business process of this digital library, we surveyed department directors and senior network technicians of technology in August 2012. Among the respondents, five are from the network department; the others work in the editorial department, the comic library, the children’s library, the department of counseling and the learning promotion center. The threat occurrence possibilities and the assignment method concerning the threat impact on assets are shown in Table I. 3.3.2 Vulnerability index assignment and collection methods. To ensure the accuracy of the investigation, three different methods are used in identification and assessment of vulnerability, namely, questionnaire investigation, on-site inspection and tool test. Responses to the questionnaire can reflect librarians’ opinion of this digital library vulnerability status. On-site inspection is conducted by our research team members. The data collected by this method reflects the evaluators’ opinion of this digital library vulnerability status. Tool test uses mature software which could determine the digital library real technical vulnerability status. Questionnaires use Guttman scale form and cover two major types of vulnerability (i.e. technology vulnerability and management vulnerability), as well as some sub-types, as shown in Table II. Vulnerability questionnaire design is mainly based on vulnerability identification table of GB/T20984-2007 and the business of digital library, which could ensure the validity of the questionnaire. The vulnerability identification table of GB/T20984-2007 covers technical vulnerability and management vulnerability. In pilot investigation, we found that the respondents are only familiar with the duties and businesses of their own department. Especially, management librarians and ordinary librarians have difficulties in understanding the questions of technical vulnerability. Due to the knowledge structure differences among the library staff, we designed three different questionnaires for management librarians, the technical librarians and the ordinary librarians separately. Management librarians include library leaders and department heads, who participated in the investigation about security policies, the responsibilities of the position, safety management, education and training and so on. Technical librarians include technical department members and librarian of other departments in charge of technical work. The investigation of technical librarians involves information
Very low
Low
Medium
High
Very high
Level
1
2
3
Threats occurrence possibility Confidentiality loss rate
4
Threats occurrence possibility Confidentiality loss rate Integrity loss rate Availability loss rate
Availability loss rate
Integrity loss rate
Availability loss rate Threats occurrence possibility Confidentiality loss rate
Integrity loss rate
Integrity loss rate Availability loss rate Threats occurrence possibility Confidentiality loss rate
Threats occurrence possibility Confidentiality loss rate Integrity loss rate Availability loss rate
Index meaning
5
Assignment
High frequency (1 or more times per week); almost inevitable in most cases Once occurring, it may bring irreparable damage to asset confidentiality and business Once occurring, it may bring irreparable damage to asset integrity and business Once occurring, it may cause very serious damage to asset availability or cause long-lasting intervals High frequency (1 or more times per month); and it is likely to happen in most case Once occurring, it may cause serious damage to asset confidentiality and lead to partial function of the asset rights protection system Once occurring, it may cause serious and irreparable damage to asset integrity and business Once occurring, it may cause serious damage to asset availability or long intervals Medium frequency (1 or more times per half year); and it may occur in some cases Once occurring, it may cause some damage to asset confidentiality and affect the rights protection system Once occurring, it may cause some damage to the asset integrity and have effects on the business, but the damage can be fixed Once occurring, it may bring some damage to the asset availability or cause short intervals Low frequency; and unlikely to occur in most case Once occurring, it may cause minor damage to asset confidentiality and slightly affect the rights protection system Once occurring, it may cause minor but tolerable damage to the assets and businesses, but the damage is reparable Once occurring, it may cause minor damage to asset availability or intervals, which can be repaired right away Threats hardly occur expect in some very rare cases Once occurring, it may cause negligible damage to asset confidentiality Once occurring, it does not affect the asset integrity, and its effect on business can be ignored Once occurring, it does not affect the asset availability and will not cause intervals
Index assignment method
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Table I. Attributing method of threat occurrence possibility and its impact on asset
Table II. Vulnerability index structure and implementation levels
Organizational management
Management vulnerability Technical management
Application middleware Application system
Database software
System software
Network structure
Technical vulnerability Physical environments
Physical and environmental security, communications and operations management, system development maintenance, access control Security policy, organization capability to solve security, asset classification and control, personnel security, compliance with organization
Fireproofing, power supply and distribution, anti-static precautions, grounding and lighting protection measures of the room Network structure design, border protection, external and internal access control policy Patch installation, physical protection, user accounts, passwords, access control, etc. Patch installation, identification mechanism, password mechanism, access control, backup recovery mechanism, etc. Protocol security, transaction integrity, data integrity Audit mechanism, auditing, storage, access control policy, data integrity, password protection
Vulnerability sub-items
1–Hardly implemented 2 – Having relevant regulations and only some have been implemented 3 – Implemented but not checked 4–Implemented and checked to some extent 5 – Fully implemented good enough to be set as a model for other digital libraries to follow
Implementation level
478
Vulnerability categories
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security management, information security operation maintenance and so on. The ordinary librarians include formal staff of this library who are investigated about responsibilities of the position and operations manual and so on. Altogether 15 management librarians, 7 technical librarians and 72 ordinary librarians were surveyed. On-site inspection is conducted on the basis of problems concerning the 11 control domain of ISO27002. To check the information security implementation of the digital library in the case study and identify its vulnerability, we visited relevant departments with specific problems in mind. We carried out the inspection in the form of observation, examinations, testing and inquiries. We are mainly concerned with the environments, the implementation, the data documents and the operation habits. The “remote security evaluation system” of NSFOCUS is used in the vulnerability scanning, which covers IP servers and desktops (www.nsfocus.com.cn/index.html). Relying on professional NSFOCUS security team, this system uses NSFOCUS Intelligent Profile, simulate penetration and other advanced technology comprehensively to find security vulnerabilities of network assets. It is one of the international leading vulnerability management products, which can find these vulnerabilities automatically, efficiently, accurately and timely. Its detection objects include a variety of mainstream operating systems (Windows, Unix, Linux, etc.), application services (FTP, WWW, Telnet, Smtp, etc.) and network equipments.In all, ten IP sections and a total of 81 hosts (servers, storage and network equipment, etc.) are involved in the scanning. 3.3.3 Risk value calculation method. Asset value, threat level and vulnerability are all measured by a five-scale scoring system (1 ⫽ very low; 2 ⫽ low; 3 ⫽ medium; 4 ⫽ high; 5 ⫽ very high).Here are the specific conversion standards: when 4.2 ⬍ ⫻ ⬍ ⫽ 5, it is expressed as Level 5.When 3.4 ⬍ ⫻ ⬍ ⫽ 4.2, it is Level 4. When 2.6 ⬍ ⫻ ⬍ ⫽ 3.4, it is Level 3.When 1.8 ⬍ ⫻ ⬍ ⫽ 2.6, it is Level 2, and when 1 ⬍ ⫻ ⬍ ⫽ 1.8, it is Level 1.The result of value calculation are obtained using multiplicative method of GB/T20984-2007 and range between 1 and 25. Risk is classified into five groups according to its score range and frequency. If the score is located between 1 and 3, then the risk level is 1, which implies that the risk is very low and the business is hardly affected. If the score is located between 4 and 6, then the risk level is 2, which means that the risk is low and acceptable, and the business is a slightly affected. If the score is located between 7 and 9, then the risk level is 3, denoting that the risk is medium, and the business is affected to some extent, but there is no need to take measures. If the score is located between 10 and 14, then the risk level is 4, which means that the risk is high, and the business is affected seriously and measures need to be taken to reduce risk. If the score is located between 15 and 25, then the risk level is 5, which implies that the risk is very high, and the business is affected very seriously and some measures must be taken immediately to reduce risk. 4. Findings 4.1 Threat assessment results Based on the asset item list and threat sources acquired in the surveys, we propose an assets threats comparison table and, finally, obtained the threat assessment results of the digital library. Partial results are shown in Table III. Table III shows that the electronic resources of this digital library include purchased resources, self-built resource and Dongguan learning center resources, and they are subject communications failure, storage medium fault, computer equipment failure and
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Purchased resources (e-book, e-journal, dissertation, etc.); Self-built resources (Glorious Image Gallery of Dongguan; Enterprises Database of Dongguan); Other resources(Dongguan learning center, etc.)
Electronic resources
Table III. Partial results of the threat assessment
Resources Communications failure Storage medium fault Computer equipment failure System software failure Application software failure Database failure Denial of service attacks Destructive attacks Unauthorized access Malicious code Misuse resource Internal staff sabotage Unauthorized data citation or leak Unauthorized granting of network or device access Inappropriate configuration and operation Internal staff personal information loss Improper hardware maintenance Improper software maintenance No or wrong response and recovery Traffic overload Supply failure Inappropriate management and operation
Threats 2 3 3 3 3 4 2 4 4 4 3 4 4 4 2 4 3 2 2 2 2 3
2 2 2 3 2 3 2 2
2 2 3
4 3 3
3
3
2 4 3 3 3 4 3 4 3 4 3 4 3 3
3 3 4
4 4 3
3
4
3 4 4 4 3 4 3 4 3 4 3 4 3 2
Security properties loss rate Confidentiality integrity Availability
3 3 3 3 3 3 3 2 3 3 2 2 2 2
Occurrence possibility
480
Assets categories
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2.6 2.2 2.6
2.7 3.0 2.3
2.6
2.4
2.6 3.3 3.2 3.2 3.0 3.5 2.8 2.8 3.2 3.5 2.4 2.8 2.6 2.4
Threats score
2 2 2
3 3 2
2
2
2 3 3 3 3 4 3 3 3 4 2 3 2 2
Conversion grade
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other 22 threats. Among these threats, the conversion grade of database failure and malicious code level is 4, which indicates these two digital library threats reach high level. Furthermore, these two threats possibility reaches Level 3, which indicates these two digital library threats has medium frequency(1 or more times per half year), and it may occur in some cases. If these threats occur, then they will exert high-level influence on confidentiality, the integrity and availability of assets. Eventually, according to equation (3), these two threats score is 3.5. 4.2 Vulnerability assessment results Based on the research of digital library threat list, we propose a threat-vulnerability comparison table and, finally, obtained the assessment results of this digital library vulnerability. Partial results are shown in Table IV. As shown in Table IV, technical vulnerability of system software includes 11 sub-items such as package installation, physical protection. We obtained the vulnerability assessment of the package installation via three different methods, (i.e. on-site inspection, questionnaire and tool scanning). Using vulnerability assessment method, we get the conversed vulnerability and grade it as Level 3. Risk value distribution is shown in Figure 1. 4.3 Risk scores Threats are already related with both assets and vulnerability. On this basis, assets, threat and vulnerability are further correlated. Finally, 2,792 risk scores are obtained using the calculation method of risk assessment model. The vast majority of risks are distributed at the very low level (10.53 per cent), the low level (47.67 per cent) or the medium level (31.63 per cent).The results indicate that the information security of this digital library is relatively protected. But there still exist some hidden information security danger. In all, 10.10 per cent of risks reach the high risk level, and measures are needed to reduce the risk. In all, 0.07 per cent of risks attains to the very high level and need to be dealt with in time. 5. Discussion and conclusion 5.1 High risk threat and vulnerability distribution Two very high-level risk items exist in this digital library and are particularly reflected in physical assets(computer-server, security device-fireproof wall hardware), password attacking threat and software password protecting vulnerability. In addition, some high risks assets include software assets (44.68 per cent), physical assets (31.56 per cent) and electronic resources (23.76 per cent) as major type. The high risk items involve 26 types of threats, accounting for 44.07 per cent of the total. Among them, more than 10 high risk level items correspond to the 12 threat items (including improper hardware maintenance, internal staff sabotage, password attacking, improper software maintenance, malicious infiltrating, invasion and tampering, unauthorized access) individually. The distribution of these threats is shown in Figure 2. The high risk items fall into 13 vulnerability types, accounting for 22.8 per cent. Among them, more than ten high risk level items correspond to eight threat items (i.e. asset sorting and controlling, security strategies, visit control, back-up and recovery mechanism, system patch installation, software password protection, business continuity and system password strategies) individually. Their distribution is shown in Figure 3.
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Package installation Physical protection User account Password policy Resource sharing Event audit Access control New system configuration Registry reinforcing Network security System management
Technical vulnerability of system software
Table IV. Partial results of the vulnerability assessment
Vulnerability sub-items 3 2 2 4 3 3 3 2 2 3 3
On-site inspection 3 3 3 3 2 2 2 2 1 2 2
Questionnaire 2 2 2 2 2 3 2 2 2 2 2
Tool scanning
2.7 2.3 2.3 3 2.3 2.7 2.3 2 1.7 2.3 2.3
Vulnerability calculation value
3 2 2 3 2 3 2 2 1 2 2
Conversed vulnerability grade
482
Vulnerability categories
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terms number
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5.2 The existence of digital library information security risk Based on the previous data analysis, we found that the digital library risks are manifested mainly in the following aspects: • Information security management strategies or mechanism are the guidelines and methodology of library information security management. Due to lack of systematic and independent information security management plans, all important assets are likely to face various threats in every operational stage or worsen the vulnerability in assets even further. • The access strategies of systems, software, network, database and other important assets are flawed or only partially enforced in some cases. Sometimes, the supervision mechanism does not work sufficiently. All these might cause threats (i.e. network security, improper operation, unauthorized access) to software resources, electronic resources, data files and other forms of assets.
1 terms number
2
3
4
5
6
7
8
9
10
11 12 13 14 15
16 120 158 239 455 637 494 226 163 173 16 66 12 15
2
Figure 1. Digital library information security risk score distribution
Figure 2. Threat distributions of high risk items
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Figure 3. Vulnerability distributions of high risk items
• Detailed asset sorting and controlling plans, asset management and ownership rights rules are lacking in this digital library; besides, the management is rather inefficient. That would result in threats like poor maintenance and poor management of important hardware assets. • Backup mechanism is flawed. All the systems and data are backed up locally and are stored in the same computer room. Once a fire, a communication failure or other threats occur, all kinds of facilities, especially the electronic resources and data document assets will be put at serious risk. • Both servers and personal computers are vulnerable due to delayed updates of systems. Moreover, no strict access control policy is established when systems fail to upgrade in time for certain reasons. Once exploited by hackers, these problems would cause such threats as malicious infiltration, invasion, tampering, malicious code, destructive attack and vulnerability detection to electronic resources, data files and software or hardware. • As for the servers and the personal servers of this library, there exist vulnerabilities like poor password management, poor identity authentication, unchanged passwords and shared accounts and passwords. All would cause threats like malicious damage, improper operation and misoperation and place electronic resources and data files at risk. • There is no such measure as business continuity management plans in this library; thus, it is hard to respond quickly and appropriately to the risks once severe information security issues occur. 6. Implication and suggestion 6.1 Practical implication Based on the risk assessment results and analysis, and considering the current asset operation of this digital library and the safety requirements, we are certain that this digital library should focus on the following risk-control aims:
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• Guarantee the physical security of this digital library, that is, the security of various electronic resources, data files, servers, network equipment, office equipment and infrastructure. Protect the library objects from threats of fire, vandalism and mal-operation so as to ensure the normal operation of the digital library. • Guarantee the system software security of the digital library, that is, the security of portal, service platform, operational system, access control system and business management system and so on. For this purpose, it is necessary to enhance the management in passwords, mails, downloading, software installing of various systems, the management of removable storage media. It is also importance to improve the anti-virus and the anti-attack ability of all systems and increase staff members’ safety awareness and operation proficiency. • Guarantee the safety of the library data, that is, the information safety of all users, staff members and relevant data. To achieve the above aim, it is necessary to enhance password management and train staff intensely in security awareness, operation skills and maintenance knowledge. It is also important to train users more in using and accessing systems.
6.2 Theoretical enlightenment The fact that the assessment went on well and that, as indicated by our interview, the results are well accepted by both management and technicians verifies the feasibility of the digital library information security risk assessment method adopted in this paper. First, conforming with ISO 27000 series standards, this paper follows the standard principle of risk assessment in terms of methods and process. Second, covering all the digital library assets and the threats and the vulnerabilities they face, this assessment sticks to the holistic principle of risk assessment. Third, high operability of the assessment method and process ensures the completion of the job and also invokes the controllability principle of risk assessment. Fourth, the assessment process meets the principle of minimal influence. Nevertheless, it is important to note that risks control is needed after the completion of assessment, and then, another round of assessing begins. Only different rounds of assessment are carried out, can a high information security level of digital library be maintained. In other words, it is essential to adopt a PDCA cycle mode in managing digital library information security. Although this research is a case study on a typical domestic digital library information security risk assessment using the multiplication of GB/T20984-2007, the conclusion cannot be extended to other digital library information security risk management. This research finds that assessment of digital library information security involves many types of assets, threat and vulnerability .When correlated with each other, they give rise to 2,792 risks. If such assessment is conducted in all libraries, then it would be very time-consuming considering that respondents have to answer a large number of questions. By conducting more interviews and sending out more questionnaires, we will look further into assets types, assets significance, threat types, threat influence and establish an assessment scale suitable to all libraries, hence increasing the operability and convenience of the digital library assessment mode.
Digital library information security 485
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References Abioye, A.A. and Rasaki, O.E. (2013), “Survey of security challenges in university libraries in southwest Nigeria”, Library & Archival Security, Vol. 26 Nos 1/2, pp. 1-13. Adam, N., Atluri, V. and Bertino, E. and Ferrari, E. (2002), “A content-based authorization model for digital libraries”. IEEE Transactions on Knowledge and Data Engineering, Vol. 4 No. 2, pp. 296-315. Ajegbomogun, F. (2004), “Users’ assessment of library security: a Nigerian university case study”, Library Management, Vol. 25 Nos 8/9, pp. 386-390. Anday, A., Francese, E., Huurdeman, H., Yilmaz, M. and Zengenene, D. (2012), “Information security issues in a digital library environment: a literature review”, Bilgi Düünyası, Vol. 13 No. 1, pp. 117-137. Anderson, L. (1997), “Digital libraries: a brief introduction”, ACM SIGGROUP Bulletin, Vol. 18 No. 2, pp. 4-5. Balas, J. (2005), “Close the gates, lock the windows, bolt the doors: securing library computers”, Computers in Libraries, Vol. 25 No. 3, pp. 28-30. Bello, M. (1998), “Library security, material theft and mutilation in technological university libraries in Nigeria”, Library Management, Vol. 19 No. 6, pp. 379-383. Bowers, S. (2006), “Privacy and library records”, The Journal of Academic Librarianship, Vol. 32 No. 4, pp. 377-383. Fan, Q. (2009), “A research in risk assessment of telecommunication industry information security”, Master Dissertation, Tianjin University, Tianjin. Fox, R. (2006), “Vandals at the gates”, OCLC Systems & Services, Vol. 22 No. 4, pp. 249-255. Gressel, M. (2014), “Are libraries doing enough to safeguard their patrons’ digital privacy”, The Serials Librarian, Vol. 67 No. 2, pp. 137-142. Holt, E. (2007), “Theft by library staff”, Bottom Line: Managing Library Finances, Vol. 20 No. 2, pp. 85-92. Huang, S. (2011), Information Security Management of Digital Library, Nanjing University Press, Nanjing. Ismail, R. and Zainab, A. (2013), “Assessing the status of library information systems security”, Journal of Librarianship and Information Science, Vol. 45 No. 3, pp. 232-247. James, Y. and Thong, H. (2002), “Understanding user acceptance of digital libraries: what are the roles of interface characteristics, organizational context, and individual differences”, International Journal of Human-Computer Studies, Vol. 57 No. 3, pp. 215-242. Kuzma, J. (2010), “European digital libraries: web security vulnerabilities”, Library Hi Tech, Vol. 28 No. 3, pp. 402-413. Lesk, M. (1997), Practical Digital Libraries: Books, Bytes, and Bucks, Morgan Kaufmann Publishers, San Francisco, CA. Lopez, K. (2003), “Making the library of congress secure: innovation and collaboration”, Journal of Library Administration, Vol. 38 Nos 3/4, pp. 169-173. Maidabino, A. and Zainab, A. (2011), “Collection security management at university libraries: assessment of its implementation status”, Malaysian Journal of Library & Information Science, Vol. 16 No. 1, pp. 15-33. Michalko, J., Malpas, C. and Arcolio, A. (2010), “Research libraries, risk and systemic change”, available at: www.oclc.org/content/dam/research/publications/library/2010/2010-03.pdf? urlm⫽162937 (accessed 12 May 2015).
Myongho, Y. (2011), “Balanced security controls for 21st century libraries”, Library & Archival Security, Vol. 24 No. 1, pp. 39-45.
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Further reading General Administration of Quality Supervision (2007), “Inspection and quarantine of the People’s Republic of China, standardization administration of the People’s Republic of China”, GB/ T20984-2007 Information security technology-Risk assessment specification for information security, China Standardization Press, Beijing. Huang, S., Mao, Y. and Xiong, J. (2010), “Assessment of information security risk in digital libraries”, New Technology of Library and Information Service, Nos 7/8, pp. 33-38. About the authors Zhengbiao Han is a Lecturer at the College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China. His current research interests include information security of digital libraries and information user behavior. Shuiqing Huang is a Professor at the College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China. His current research interests include information security of digital libraries and information retrieval. Shuiqing Huang is the corresponding author and can be contacted at: [email protected] Huan Li is a Graduate Student at the College of Information Science and Technology, Nanjing Agricultural University, Nanjing, China. Ni Ren is a Doctoral Student at the College of Information Science and Technology, Nanjing Agricultural University. She is also a Librarian of Institute of Agricultural Economics and Information, Jiangsu Academy of Agricultural Sciences, Nanjing, China.
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1. WuZongda, Zongda Wu, ZhengChengren, Chengren Zheng, XiejianJian, Jian Xiejian, ZhouZhifeng, Zhifeng Zhou, XuGuandong, Guandong Xu, ChenEnhong, Enhong Chen. 2018. An approach for the protection of users’ book browsing preference privacy in a digital library. The Electronic Library 36:6, 1154-1166. [Abstract] [Full Text] [PDF]
THE IMPACT OF CLOUD COMPUTING ON THE FUTURE OF ACADEMIC LIBRARY PRACTICES AND SERVICES
New Library World The impact of cloud computing on the future of academic library practices and services Judith Mavodza,
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The impact of cloud computing on the future of academic library practices and services
132 Received 10 October 2012 Accepted 21 November 2012
Judith Mavodza Library, Zayed University, Abu Dhabi, United Arab Emirates Abstract
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Purpose – The purpose of this paper is to discuss issues involved in navigating the modern information environment where the relevance of cloud computing is unavoidable. This is a way of shifting from the hardware and software demands of storing and organizing data, to information access concerns. That is because with the exponential growth in information sources and all accompanying complexities, the limited capacity of libraries to host their own in its entirety necessitates opting for alternatives in the cloud. Design/methodology/approach – A review of current literature about the topic was performed Findings – Literature used reveals that currently, libraries are using the cloud for putting together user resources, i.e. using Software as a Service (SaaS), such as in library catalogues, WorldCat, Googledocs, and the aggregated subject gateways like SUMMON, and others; the web Platform as a Service (PaaS) as in the use of GoogleApp Engine; or Infrastructure as a Service (IaaS) as in the use of D-Space, FEDORA, and others. The cloud is confirmed as a facilitator in storing and accessing information in addition to providing a unified web presence with reduced local storage capacity challenges. Originality/value – The value of these findings is to remind librarians of the shift in focus towards which devices provide the easiest access to data and applications. This is one of the reasons they in many instances are currently having to address issues relating to the use of electronic media tools such as smartphones, iPad, e-book readers, and other handheld devices. The largely borderless information resources also bring to the forefront considerations about digital rights management, fair use, information security, ownership and control of data, privacy, scholarly publishing, copyright guidance, and licensing that the librarian has to be knowledgeable about. It has become necessary for librarians who make use of commercial cloud services to be conversant with the implications on institutional data. To avert the ever present dangers and risks involving cyber-security, it is usually practical for institutions to keep policies, procedures, fiscal, and personnel data in private clouds that have carefully crafted access permissions. Being aware of these implications enables thoughtful, adaptive planning strategies for the future of library practice and service. Keywords Academic libraries, Cloud computing, IaaS, PaaS, SaaS, Interoperability, Web 2.0 Paper type General review
New Library World Vol. 114 No. 3/4, 2013 pp. 132-141 q Emerald Group Publishing Limited 0307-4803 DOI 10.1108/03074801311304041
Introduction The context within which academic libraries are currently operating requires a recognition of the following issues surrounding the exponential growth of information and information resources as well as knowledge-driven academic environments: . The academic world is constantly changing, becoming more global and new competitors are emerging. . There are new types of innovation where people are willing to take risks and experiment with new ideas in the way education is provided and academic pursuits investigated.
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.
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.
Library user logic rather than librarian logic has become more important as a way of understanding the needs of library users. It is essential constantly to seek context specific appropriate technologies for the library. The acquisition and utilization/implementation of new information and communication tools and technologies sets an enabling environment for innovative methods of operation in both the library and the entire institution.
With the current discontinuous IT revolution, libraries have become one of the spaces in which most academic institutions use the cloud to cope with the new information environment in meeting the needs of patrons. Librarians therefore need to widen their skills set and think more openly so as to understand and cope because these kinds of developments affect their professional environment in irreversible ways. Dealing with a broader range of information resources and services than by traditional means makes it important for them to be well conversant about the choices and options available to them. This paper addresses the issues involved in academic libraries operating in this environment, and then suggests the implications and recommendations for them to be resilient. The resilient library Resilience is important if a library has to maintain its character and yet be adaptive to inevitable and unpredictable changes that happen at an accelerated pace. This calls for librarians being able to provide a wide variety of information from an equally varied selection of sources and formats, particularly with the prevalence of cloud use. The implication is for librarians to work as complementary teams within the library and with other relevant departments/institutions. It is realized that cloud computing enables new streamlined workflows for cooperation and community building among libraries (Goldner, 2011). In all activities, keeping and tracking activities enables the study of trends and feedback for the enhancement of service. Efficient communication networks are therefore essential for the benefit of the system as that enables the making of informed choices and decisions. This is much like the use of knowledge management principles. An increase in information flow is enabled by the use of IT networks and that is one of the reasons there is an increased reliance on the use of the cloud. Using the cloud Cloud computing is the delivering of hosted electronic services over the internet. According to Scale (2009, p. 10), it is: The sharing and use of applications and resources of a network environment to get work done without concern about ownership and management of the network’s resources and applications [. . .] data are no longer stored on one’s personal computer, but are hosted elsewhere to be made accessible in any location and at anytime.
Models for cloud use vary depending on defined needs. They are Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS) (Cave et al., 2012). The extent to which the end user has control varies in terms of applications, hosting environment, storage, operating systems, servers, network, and cloud infrastructure. For example, the user has minor control over applications if using
Impact of cloud computing
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Software as a Service (SaaS), and no control over the hosting environment, storage conditions, the operating system, network and servers, and the cloud infrastructure. When operating in the cloud Platform as a Service (PaaS), the user has control over the applications and may have some control over the hosting environment, but no control over storage conditions, the operating system, network and servers, and the cloud infrastructure. If using the cloud Infrastructure as a Service (IaaS), then the user does not have control of the cloud infrastructure, but can control the applications, the hosting environment, storage, servers, and the network. In explaining the constantly evolving internet web, Cave et al. (2012) point out that there are more models developing in the form of Hardware as a Service (HaaS), and Communications as a Service (CaaS). However, in libraries, the concern is on patron or end-user satisfaction, therefore the models matter mostly insofar as they impact on the ability of effective quality service provision. When discussing cloud computing in a library context, it is important to define how the models apply it terms of the types of services involved, the infrastructure used, the platform on which applications are built and the associated applications. The use of a Platform (PaaS) refers to a situation where software already exists, such as when the library uses an Integrated Library System whether it is open source such as Koha or Greenstone, or proprietary such as Millennium Innovative Interfaces or SirsiDynix, library catalogues, subject catalogues, OverDrive, Googledocs, and WorldCat. Software as a Service (SaaS) can be viewed in the use of LibGuides, the library catalogue, WorldCat, OverDrive, aggregated subject gateways that support systematic unified web-scale resource discovery such as SUMMON (a ProQuest business), Ebsco Discovery Service, Primo Central (Ex Libris), Free and Open Source Software (FOSS), Citation Management software. From the examples, it is apparent that, because library systems use both platforms and software, there is sometimes no demarcation between PaaS and SaaS. Services that are referred to as cloud-based also include the provision of actual resources, for example, OverDrive e-books, research guides and online reference services that are ready for use. The function of cloud applications is exemplified by Google Docs or library e-book readers such as for ebrary books or Safari books that are accessed with a web browser. Cloud Infrastructure (IaaS) refers mostly to the space/time that users can buy to use external servers for electronic storage as in institutional digital repositories or institutional archives. It is also the infrastructure that enables open-source software for running repositories, for example, D-Space, FEDORA, Eprints, or hosted software packages such as Digital Commons, and SimpleDL. Electronic storage can be local or remote. When it is local to an organization, the location is a local private server within the organization’s own local or wide area network. In an education institution, items that are stored include employee and student records, student retention data, budgetary data, and so on that are confidential and not intended for public access or use. However, it is not always practical to expand server storage capacity continuously. Decisions are therefore made to buy server space from cloud providers to accommodate some collections for remote storage. Scholarly works and historical documents are examples of collections that most readily fit with digitizing for repository and/or archive creation. Libraries are also using and accessing more digital collections that are not necessarily their own, and are becoming involved with digitizing some publications/collections too. An organization determines access levels to its repository – ranging from completely open access to highly private. In the library
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context, the increased use of digital resources has come about because it is practically impossible to purchase every single piece of material that library users need because of budgetary, physical and virtual space limitations. This is happening in a fast changing information environment that enables different approaches to using technology. At this juncture, the semantics, human, legal, and international aspects involved because of the use of the cloud need to be addressed. The reason is that libraries have stepped and are increasingly stepping into the realm of digital librarianship as well as platforms that extend IT’s existing capabilities, and this extensively depends on using the cloud. In terms of the use of semantics, the research of Alemu et al. (2012, p. 8) suggests that “the proliferation of metadata standards has brought interoperability problems between disparate digital libraries”. Adding to this discussion is a point raised by Diekema (2012, p. 165) that “enabling users to search across languages requires translation resources to cross the language barrier, which can be challenging depending on the language and resource availability”. In the meantime, researchers in the academic community are concerned more with the knowledge they have access to than in whether it is the result of the work of a librarian, a records manager, an archivist, or any other information source. They want to use cloud computing applications as a service (PaaS and IaaS) that enables them to access and or/use desired information and data with no portability hurdles that come as a result of the existence of products and services from different vendors or providers. This point is closely linked to the human factor in that users need uncomplicated ways of accessing data or information regardless of geographical boundaries. The moment patrons start using information in a borderless context, legal concerns that start at the level of local control and ownership to the same concerns on an international scale feature. Thus, given the fact that cloud computing is not necessarily restricted by boarders between countries, Cervone (2010) points out that the major problem with clouds hosted internationally is that the application and data are subject to the laws and policies of the host nation. For example, provisions of the Patriot Act (Providing Appropriate Tools Required (to) Intercept (and) Obstruct Terrorism Act of 2001) are strictly applied in the US, but in varying degrees by different countries (Xhelili and Crowne, 2012). Similarly, not all applications in one country can always be hosted in another because of restrictions in the export of computer system technology (Cave et al., 2012; Harsh et al., 2012). Additionally, Kshetri (2010, p. 53) suggests that “cloud providers from developing countries such as China and India might face barriers to internationalization activities, particularly because security is among the most important concerns for cloud adoption”. But at the same time the cloud computing concept seems to be providing new opportunities for library application development as libraries gradually evolve from information to knowledge commons spaces. Practically for information professionals, “there may still need to be decisions made as to some categories of content which unrestricted access cannot be given” (Genoni, 2004, p. 304). For example, of concern is how to integrate in-house services with cloud services which include identity management and branding in the cloud. It is therefore essential to be in control of cloud computing platforms of choice. However, if those systems are managed by professional systems engineers, then it may make sense to migrate to external services. While using cloud computing infrastructure or platform as a service (SaaS, IaaS and PaaS) in creating repositories, libraries and information centers use software for
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storing specified data using the tools the provider of the server provides. They can, however, determine the configuration and access settings because the software allows for it. This makes the development of interoperable digital libraries an important area of operational focus (Harsh et al., 2012). What that refers to is an environment whereby many different components of different libraries are viewed as one, but with the autonomy of the libraries involved still maintained. Where a repository has been created, it is important for libraries to be cognizant of conservation and preservation of its content because it “should be cumulative and perpetual [. . .] open and interoperable” (Genoni, 2004, p. 304). This is a role that librarians are capable of participating in or handling because that is incorporated in library collection development policies, with no recourse to IaaS and PaaS providers. The use of repositories has the potential to facilitate sharing of resources in educational research through portals that are modeled as gates to several repositories. The real snag for now is that there are no interoperable cloud provider standards yet for security functions to protect those knowledge assets. This is confirmed by Dikaiakos et al. (2009) who point out that this is a currently on-going discussion, and reinforced in another publication by Harsh et al. (2012) that cites the issues, challenges and possible solutions involved in making standardization achievable. One of the reasons that this security concern arises is a point raised by Kshetri (2010, p. 54) that it is sometimes “tempting for global cloud players to use cheaper hosting services in developing countries”, and cybercriminals take advantage of any existing loopholes. Included in this debate are matters relating to intellectual property control, data protection and privacy laws imposed by various governments, variations in cloud application programming interfaces (APIs) provided by different providers to end-users. APIs provide the interface for file and document storage services, queue services, and actual infrastructure. Thomas (2011) confirms that privacy, security, anonymity, telecommunications capacity, government surveillance, reliability, and liability, among others, are of great concern in the context of cloud computing usage. In this debate, the role of the librarian is as a participant in discussing privacy and ownership of information issues. An example that demonstrates a progressive initiative is the June (2012) agreement between EBSCO Publishing (EBSCO Discovery Service – EDS) and OCLC WorldShare Management Services to become interoperable (OCLC, 2012). It is intended for OCLC connected libraries to use EDS as the discovery layer and WorldShare the library management system. Other initiatives that Goldner (2011, p. 26) notes include: the National Library of Australia’s (NLA) Trove, that uses the web to combine the collections of Australian libraries with other important Australian and international collections and information sources such as Wikipedia and opens up much of this content so the public can tag, edit, collect and review it; the Bayerische Staatsbibliothek in Germany, and Bibsys in Norway; the Hathi trust that is building a repository of digitized books and journals from major research libraries in the US; the OAISTER which is a service started by the University of Michigan and now managed by OCLC that harvests all the major digital repositories around the world; and European which is gathering the digitized collections from Europe’s galleries, libraries, archives and museums. This means that librarians’ jobs and responsibilities are irreversibly evolving, both at the stage of training and in practice, particularly now that they need an understanding of these issues to help them in addressing the reality of their use of the cloud. In this context, librarians in many developing world libraries are disadvantaged
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due to encountering connectivity challenges because funding cloud computing platforms or enhancing bandwidth are not always priorities in these regions as there are more immediately urgent problems for funders to deal with such as hunger. In fact, Odongo (2010, p. 74) suggests that these regions exist “beneath the bandwidth” because they are left behind due IT challenges. Most modern library users are comfortable using the spaces provided by information-based industries (Anderson, 2007; Harris and Lessick, 2007). Collaborative, interactive workspaces have become relevant in this information environment (Anderson, 2007; Sadeh, 2008) and librarians are finding ways of making use of the new technologies to best advantage, for example with the use of handheld devices (such as iPads and Kindle) for attending to patron needs, or assisting them in creating useful research-related tags. Patron demand now takes precedence to library logic. This is similar to a product marketing perspective where customer needs are always paramount. According to Abram (2007, p. 35): We’re moving from a technology-centric strategy to one in which the real needs of our clients must predominate. Aligning technology with user behaviour no longer suffices to ensure success. We need to understand, and understand deeply, the role of the library in our end-users’ lives, work, research, and play.
The concerns of librarians are centred on how they can use the cloud to both personalise and localize the user’s information seeking experience (Abram, 2010; Gerke and Maness, 2010). This leads to the need to understand the ways in which modern library users interact with the cloud, and how library services may need to be modified to fit into the emerging user patterns. These user patterns have a bearing on the collaboratory work of faculty and librarians. The library’s participation in these platforms helps define the evolving role of the library that is now complemented by the virtual space. Librarians and other educators are concerned with ways of taking advantage of the popularity of handheld devices. However, a study by Ting (2012) proved that the use of mobile devices needs to be contextualized for them to be perceived by students as relevant to learning situations. This is confirmed by an EDUCAUSE (2012) study of undergraduate students and information technology that revealed the tendency for students to use social networks for interacting with friends more than for academic communication. Among its recommendations is not to focus efforts on using social networks and telephone conversations to interact with students. What this means is that assuming that students will use the academic cloud because they are already using modern technology is not necessarily accurate. One of the ways of making handheld devices relevant as a way of taking advantage of their being already a platform for cloud access is to upload course-related content on individual devices, but also accommodating the licensing and copyright issues. The use of cloud computing services such as those from Google, Amazon, Salesforce, Apple’s iCloud, Microsoft and other apps is current, so librarians and educators have to step away from their traditional methods to those that embrace these for teaching and instructional purposes. Although Goldner (2011) mentions that online public access catalogues (OPACs) are not integrated with most information seekers’ common workflows, with contextualization in mind, librarians can demonstrate to library users ways of using Web 2.0 functionality such as tagging (so that they have a set of records that they prefer to use readily accessible to them in a library catalogue tag cloud). They can also encourage the use of sites such as Flickr or Picasa Web for sharing pictures that are
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relevant to their activities, or have a library social networking account such as Twitter, Facebook or MySpace or delicious.com or any of the numerous social networking platforms that are currently in use, to reach out to those that are comfortable with use of these facilities. For example, by creating a free account in delicious.com, it is possible to put together all resources including library links, useful websites, web quests, Facebook, and all links that an individual may want. Even a tagged library catalogue can be included as a tag in delious.com. This is the use of Software as a service (SaaS). In using the library website, if tagging is put in place as a function, its use would be based on lessons gained from information literacy exercises. Macgregor and McCulloch (2006) suggest that tagging can be an effective method of organizing resources that faculty needs to support teaching, and that can in the process replace traditional subject guides. By using a platform such as delicious.com that allows cloud computing, librarians can actually invite each other into a specified closed network, add useful resources to it in a non-formal but constructive way, and in the process be accumulating knowledge for practical use. Another option may be to use Apple’s iCloud where users are able to store and interact with music, videos, contacts, calendar, mail, photos, apps, books, documents, and device backup. The author of this paper suggests that librarians and faculty co-teach some sessions in the library if they are not already doing so, where they guide students in finding and accessing materials. This helps encourage discussions about ethical and legal issues associated with acquiring and using borrowed text, images, and a whole range of material that users have access to and can manipulate in the cloud as a part of becoming information literate scholars. This collaboration enables the seamless embedding of information literacy instruction into the curriculum. Information literacy features consistently because if aligned closely with the activities that take place in classroom processes, then it ceases to be isolated from the whole big picture of teaching and learning. In other words, librarians “support transformation, not transactions” (Abram, 2010). Implications Acquiring records and documents can be a confusing and confused process. In order to avoid having irrelevant stock, it is important to have a library acquisitions policy that specifies what needs to be acquired (Cave et al., 2012; Pinfield, 2005). Content concerns raised by Cave et al. (2012) and Genoni (2004) require consultation with legislation or the legal office of the institution. This is where the type of records and length of time for keeping them is determined, and policy put in place. In a fast changing IT environment, the technology for storage of electronic material has a tendency to change fast, raising file format concerns, creating the need for regular back-ups, and concerns about reducing data loss. Whichever way one considers the issue, storage and access concerns are central, leading to the consideration to make the cloud a viable option. Trehub and Wilson (2010, p. 245) also point towards a cause for anxiety in many instances resulting in institutions settling for the cloud choice that: Digital files are inherently susceptible to decay, destruction, and disappearance. Given the vulnerability of digital content to fires, floods, hurricanes, power blackouts, cyber attacks, and a variety of hardware and software failures, cultural memory organizations need to begin incorporating long-term digital preservation services for locally created digital content into their routine operations, or risk losing that content irrevocably. The advent of a “digital dark age” is not just a clever concept; it is a real danger.
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Issues relating to the electronic storage and management of information and knowledge require the involvement of network administrators. This demands the institutionalization of standardized protocols for storage, access, and disposition of the documents and records. This has become more evident in the wake of an instance such as the much publicized WikiLeaks saga which raised concerns about its impact on security of information, even if it affected particularly the defense and security industries. It is important that access policies be always clearly defined, verified and enforced if an organization resolves to encourage the systematic storage of knowledge assets, and even more diligently when it is in the cloud to avoid knowledge loss, abuse, and misuse. This includes “fair use” which involves both librarians and the legal office so that institutional policy on copyright and academic integrity is clearly spelt out. The institution as a whole needs institutional management support to put the practice to use. The implications of using the cloud also include training and educating educational leaders and managers. The institution needs to be clear about the implications of considering cloud computing, especially when it comes to privacy and control of data, given the fact that IT components such as clients, servers, storage, and networks in an organization can be virtualized. This impacts embedding links, podcasts, videos, wikis, etc. in standard operational processes and providing ready access to those who need it. There is, therefore, the need to use standardized, collaborative toolsets associated with relevant computer applications, for example the ability to open files, cutting and pasting, and so on. It seems practical to use open architectures to permit access to contextual knowledge and searching across boundaries and departments as well as portals that permit single individualized sign-on authentication for all eligible users, including partners, i.e. interoperability and portability. To summarize, while cloud computing is a reality that continues to be explored and used, some of the advantages of using it include reduced costs to users, greater efficiency due to online ready availability, increasingly enhanced security and data protection, easier collaboration, easier information flow and open access, green due to the reduced need for hardware and storage complications and no purchase of servers, and vendors for the most part have to deal with hardware, and operating system upgrades. Despite the mentioned advantages, there are areas of concern that are current. These include the feeling of loss of control and apprehension about data ownership that many users have, data security anxiety, concerns with privacy especially patron data, interoperability is not always guaranteed, usability and standardization issues continue to be under discussion. In addition, libraries have to be conscious of bandwidth requirements and backup storage costs. In some parts of the world, there are disruptive power outages and lack of infrastructure. There is also the question of what happens if a vendor goes out of business, or if a library fails to pay service costs. Carefully crafted service level agreements are normally the solution whenever possible, making it necessary for librarians to be always in the know. Conclusion The borderless nature of information makes questions of standardization and interoperability worthy of continuous discussion, and essential to come up with practical solutions in accessing, protecting and securing information and knowledge assets. While librarians are largely consumers of cloud computing services, an
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understanding of their operations is important for effective and efficient use. This includes the advantages and disadvantages or challenges involved. Even in technologically backward or inefficient environments, librarians need to be knowledgeable about these issues so that they can maximize what limited services and resources they have and, at the same time, be prepared to handle discontinuous change caused by information technology developments. Potentially, the cloud gives access to the vast majority regardless of where they are geographically located, but the challenge in some developing world areas is with inadequate IT infrastructure, data centers, and applications. However, academic libraries continue to evolve and librarians are at the forefront in maximizing the advantages of cloud services for supporting academic research purposes. References Abram, S. (2007), “Evolution to revolution to chaos? Reference in transition”, Internet Librarian International, Vol. 16 No. 8, available at: www.infotoday.com/searcher/sep08/Abram. shtml (accessed 20 September 2012). Abram, S. (2010), “It’s 2010: technologies to watch, and how to cope”, available at: www. slideshare.net/stephenabram1/click-u2010 (accessed 8 October 2012). Alemu, G., Stevens, B. and Ross, P. (2012), “Towards a conceptual framework for user-driven semantic metadata interoperability in digital libraries: a social constructivist approach”, New Library World, Vol. 113 Nos 1/2, pp. 38-54. Anderson, P. (2007), “All that glisters in not gold: Web 2.0 and the librarian”, Journal of Librarianship & Information Science, Vol. 39 No. 4, pp. 195-8. Cave, J., Robinson, N., Kobzar, S. and Schindler, H.R. (2012), “Regulating the cloud: more, less or different regulation and competing agenda”, available at: http://ssrn.com/ abstract¼2031695 (accessed 2 October 2012). Cervone, H.F. (2010), “Managing digital libraries: the view from 30,000 feet. An overview of virtual and cloud computing”, OCLC Systems & Services: International digital library perspectives, Vol. 26 No. 3, pp. 162-5. Diekema, A.R. (2012), “Multilinguality in the digital library: a review”, The Electronic Library, Vol. 30 No. 2, pp. 165-81. Dikaiakos, M.D., Pallis, G., Katsaros, D., Mehra, P. and Vakali, A. (2009), “Cloud computing: distributed internet computing for IT and scientific research”, IEEE Internet Computing, September/October, pp. 10-13. EDUCAUSE Center for Applied Research (2012), “The ECAR study of undergraduate students and information technology”, EDUCAUSE, Washington, DC, available at: www.educause. edu/library/resources/ecar-study-undergraduate-students-and-information-technology2012 (accessed 10 October 2012). Genoni, P. (2004), “Content in institutional repositories: a collection management issue”, Library Management, Vol. 25 No. 6, pp. 300-6. Gerke, J. and Maness, J.M. (2010), “The physical and the virtual: the relationship between library as place and electronic collections”, College & Research Libraries, Vol. 71, pp. 20-31. Goldner, M. (2011), “Winds of change: libraries and cloud computing”, Multimedia Information & Technology, Vol. 37 No. 3, pp. 24-8. Harris, A. and Lessick, S. (2007), “Libraries get personal: Facebook applications, Google gadgets, and MySpace profiles”, Library Hi-Tech News, Vol. 24 No. 8, pp. 30-2.
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Harsh, P., Dudouet, F., Cascella, R.G., Je´gou, Y. and Morin, C. (2012), “Using open standards for interoperability – issues, solutions, and challenges facing cloud computing”, available at: arXiv:1207.5949 (accessed 9 October 2012). Kshetri, N. (2010), “Cloud computing in developing economies”, IEEE Computer, Vol. 43 No. 10, pp. 47-55. Macgregor, G. and McCulloch, E. (2006), “Digital directions: collaborative tagging as a knowledge organisation and resource discovery tool”, Library Review, Vol. 55 No. 5, pp. 291-300. OCLC (2012), “OCLC and EBSCO develop partnership to offer interoperability of services for libraries and increased options for discovery”, available at: www.oclc.org/news/releases/ 2012/201240.htm (accessed 9 October 2012). Odongo, J.R.I. (2010), “Beneath the bandwidth: exploring Africa’s information divide”, Proceedings of DIS 11th Annual Conference, Department of Information Studies, University of Zululand, pp. 74-82. Pinfield, S. (2005), “A mandate to self-archive? The role of open access institutional repositories”, Serials, Vol. 18 No. 1, pp. 30-4. Sadeh, T. (2008), “User experience in the library: a case study”, New Library World, Vol. 109 Nos 1/2, pp. 7-24. Scale, M-S.E. (2009), “Cloud computing and collaboration”, Library Hi Tech News, Vol. 26 No. 9, pp. 10-13. Thomas, P.Y. (2011), “Cloud computing: a potential paradigm for practicing the scholarship of teaching and learning”, Electronic Library, Vol. 29 No. 2, pp. 214-24. Ting, Y.L. (2012), “The pitfalls of mobile devices in learning: a different view and implications for pedagogical design”, Journal of Educational Computing Research, Vol. 46 No. 2, pp. 119-34. Trehub, A. and Wilson, T.C. (2010), “Keeping it simple: the Alabama Digital Preservation Network (ADPNet)”, Library Hi Tech, Vol. 28 No. 2, pp. 245-58. Xhelili, B. and Crowne, E. (2012), “Privacy and terrorism review – where have we come in 10 years?”, Journal of International Commercial Law and Technology, Vol. 7 No. 2, available at: http://ssrn.com/abstract¼2029673 (accessed 10 October 2012). About the author Dr Judith Mavodza is Assistant Professor, Instruction Reference Librarian at Zayed University, Abu Dhabi, UAE. Current work includes instruction and working with LibGuides as liaison librarian for supporting research needs of the academic community. Areas of research interest include reference and instruction, marketing and assessment of library services, professional development of librarians, and knowledge management. She is an Editorial Advisor to the Evidence Based Library and Information Practice Journal, and is a published author of several scholarly journal articles as well as a presenter at a number of professional/academic conferences. Her professional membership includes the Information Literacy Network (ILN) of the Gulf Region, Zimbabwe Library Association, and she participates in events of the UAE Advanced Network for Research and Education (Ankabut). She completed a BSc (Honors) Sociology at the University of Zimbabwe (Harare), a Post-Graduate Diploma in Library Studies at the University College London (UK), MA in Library Studies at the University of London (UK), and a Doctor of Literature and Philosophy in Information Science at the University of South Africa (Pretoria).
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This article has been cited by: 1. Fabiana John Tonding, Samile Andréa de Souza Vanz. 2018. Plataformas de Serviços de Bibliotecas: a evolução dos sistemas para gerenciamento de bibliotecas. Perspectivas em Ciência da Informação 23:4, 73-96. [Crossref] 2. RodriguesCharles, Charles Rodrigues, Godoy VieraAngel Freddy, Angel Freddy Godoy Viera. 2018. Criteria for adoption of e-books in libraries in the context of the paradigm of cloud computing. Information Discovery and Delivery 46:3, 161-172. [Abstract] [Full Text] [PDF] 3. HarrisSasekea, Sasekea Harris. 2016. Distinctive services in academic librarianship. New Library World 117:9/10, 596-625. [Abstract] [Full Text] [PDF] 4. YuvarajMayank, Mayank Yuvaraj. 2016. Library automation with cloud based ILMS Librarika: case study of Central University of South Bihar. Library Hi Tech News 33:7, 13-17. [Abstract] [Full Text] [PDF] 5. A. A. Stukalova, A. E. Guskov. 2016. Publications on the use of cloud technologies at libraries. Scientific and Technical Information Processing 43:1, 47-57. [Crossref] 6. Mayank Yuvaraj. 2015. Problems and prospects of implementing cloud computing in university libraries. Library Review 64:8/9, 567-582. [Abstract] [Full Text] [PDF] 7. Sever Bordeianu, Laura Kohl. 2015. The Voyage Home: New Mexico Libraries Migrate to WMS, OCLC's Cloud-Based ILS. Technical Services Quarterly 32:3, 274-293. [Crossref] 8. Mayank Yuvaraj. 2015. Cloud Computing Software and Solutions for Libraries: A Comparative Study. Journal of Electronic Resources in Medical Libraries 12:1, 25-41. [Crossref] 9. Navin Upadhyay. Trends that will affect technology and resource decision in academic libraries in near future 75-79. [Crossref] 10. Michael Kalochristianakis. 2014. A case for open, viable print accounting. New Library World 115:11/12, 515-526. [Abstract] [Full Text] [PDF]
THE INTERNET OF THINGS AND ITS IMPACT ON THE LIBRARY
The current issue and full text archive of this journal is available on Emerald Insight at: www.emeraldinsight.com/0307-4803.htm
WHAT’S NEW IN LIBRARIES
The Internet of Things and its impact on the library Bruce Massis Columbus State Community College, Columbus, Ohio, USA
Internet of Things and its impact 289 Received 23 December 2015 Accepted 23 December 2015
Abstract Purpose – The purpose of this paper is to consider the Internet of Things (IOT) and its potential impact on libraries. Design/methodology/approach – This paper presents a literature review and a commentary on this topic that have been addressed by professionals, researchers and practitioners. Findings – In communicating the issues when comprehending the scope of the IOT, libraries need not succumb to the sometimes near-hysteria that surrounds the rhetoric regarding security and privacy. But, librarians must actively engage in the conversation and its subsequent actions to respond to patrons who use library networks and devices with calm, logical and transparent answers to those questions concerning what they are doing to ensure that security and privacy vulnerabilities are regularly addressed. Originality/value – The value in concentrating on this topic is to provide background and suggest several approaches to security and privacy concerns regarding the IOT. Keywords Security, Privacy, Libraries, Internet, Devices, Things Paper type Viewpoint
Introduction As the world becomes more connected through the communication devices we use, as well as the common household items and systems that theoretically make our lives less stressful, there is an increased acknowledgement that this interconnected environment has entered the next phase of potential unlimited possibilities through what is commonly referred to as the Internet of Things (IOT). Consequently, it is chilling to some who have suggested that the IOT brings with it advances wherein we must also become more aware and on-guard with regard to our privacy and security as a result of the increased numbers of devices we use every day in our homes and workplaces that are often interconnected using the internet as its medium of communication. Therefore, the IOT, at the same time, must also be discussed in terms of its equivalency to the Security of Things (SOT). That being the stark reality, libraries, an industry whose dispensing of information and data must, by their very nature, incorporate appropriate protections for their patrons, must inevitably be keenly aware of their role in this new reality: Given the amount of physical assets – books, movies, music, equipment, and staff – in a library, (author, Daniel) Obodovski sees IOT as being extremely beneficial to libraries in terms of saving staff time and improving patron service. IOT could use patron data to make tailored recommendations, all by collecting real-time data (Pera, 2014).
While the collection of data could certainly prove one successful result of incorporating information into creating a stronger profile of library patrons, there must also be an
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awareness that the proper infrastructure must also be built to coordinate all of the elements necessary to link the systems and devices already used by the library. What comprises the IOT?
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Simply put this is the concept of basically connecting any device with an on and off switch to the Internet (and/or to each other). The IOT is a giant network of connected ‘things’ (which also includes people). The relationship will be between people-people, people-things, and things-things (Morgan, 2014).
Two identifiers regarding its importance to libraries is based on four determinants: speed of delivery, platform on which the delivery is accomplished, user expectations and trust that the device one is using is verifiable and secure. With privacy and security of the internet on everyone’s mind these days, what with reports of massive hacks, constant warnings from information technology (IT) professionals regarding vulnerabilities of networks and their impact on organizations and individuals, the trust factor guaranteed by the service providers stands as a critical bond between the provider and consumer. Libraries must be aware of the potential of the IOT to disrupt services and alert patrons to potential vulnerabilities when using their networks, their hardware and software. In a study conducted in 2014 by HP Security Research, it was reported that, as the number of electronic devices, as well as consumer items, increases, both in the home and in the workplace, there were already more than 25 obvious vulnerabilities identified per device: Nine out of 10 devices collected at least one piece of personal information through the device or related cloud or mobile app; and six of the devices had user interfaces vulnerable to a range of web flaws […] (Chickowski, 2014).
These included devices such as webcams, home thermostats, remote power outlets, sprinkler controllers, home alarms and garage door openers. So, is it impossible to consider that, as the number of smart devices we use each day function interactively, that the likelihood of these devices to ease the way we live could also disrupt our lives. What can libraries do? The question above has, in the past, led to an endless parade of science fiction novels and films (think the nightmare scenario of “Skynet” in the “Terminator” series). So as not to suggest that security issues and vulnerabilities are not being addressed by the IT community, many, of course, are, and staying ahead of the risks is a vast worldwide undertaking. Libraries need not submit to the sometimes near-hysteria that surrounds the rhetoric, but must actively engage in the conversation and its subsequent actions to, as least as much as possible, be able to respond to patrons who use the library networks and devices with calm, logical and transparent answers to those questions regarding what they are doing to ensure that security vulnerabilities are frequently addressed. Security awareness means proper security management, so the library needs to ensure that it builds and updates its plan to communicate the issues to its patrons on an ongoing basis. Responses should be crafted and updated by the library regularly, and librarians need to be able to address these concerns with their patrons with a single, confident and informed voice. On a college campus or in a municipality, this means that the library is partnering with its IT organization to understand the issues in developing a comprehensive strategy to safeguard its infrastructure from the ever-increasing potential threats from the IOT.
Descriptive and clarifying statements need to be posted on the library’s website, and the site must be maintained so that the latest information is always available. A series of lectures by experienced IT security professionals and interactive town meetings, open to all, will support comprehensive and on-going communication-sharing opportunities. Signage that encourages the patrons to ask questions and engage in the discussion can be posted in strategic areas throughout the library, and on each public computer, it should be updated regularly so that they do not become back of the background and, therefore, ignored. Too often, signage that becomes too familiar is also too easy to overlook. Handouts, available throughout the library, can be printed and also must be updated regularly. The library can also use its social networking presence on multiple social networking platforms to communicate and engage in responses to questions from the public. All of these suggestions, while obvious, still require a comprehensive and structured approach so that the issues remain in front of the patrons and that there is a common understanding that their protections as well as those of the library are of paramount concern. While developing such a systematic response to the issues surrounding the IOT environment will inevitably result in time and come at a cost, the price of ignoring the issue is much too high. “There is simply no room for lax practices, a concept that should be understood at all levels and not just among rank-and-file IT workers” (Oberman, 2014). Summary In 2013, research company Gartner predicted more than 26 billion connected devices will be in use by 2020. Recently, it has been forecast that there will be more than 25 billion connected to the internet as early as 2016. Therefore, it is more than likely that the earlier prediction will be exceeded sooner than projected. In the library, it is rare for patrons to enter the library without a handheld device, whether a smart phone, tablet, laptop or possibly even all three. Add the connectivity of these devices to those in their home and could any of these actions engaged in through their use on the library’s network either cause breach of the network or a threat to the individual’s privacy? Finally, the question must be posed. Can the IOT really be secured? Jeffrey Greene, Director of Government Affairs North America and Senior Policy Counsel for Symantec, says, “Security is typically as much an afterthought as it was in the development of the internet. Nobody conceived of the Internet’s worldwide scope. Now we have no excuse” (Rubenking, 2015). References Chickowski, E. (2014), “Internet of things contains average of 25 vulnerabilities per device”, Information Week, available at: www.darkreading.com/vulnerabilities–threats/internet-ofthings-contains-average-of-25-vulnerabilities-per-device/d/d-id/1297623 Morgan, J. (2014), “A simple explanation of ‘the internet of things’”, Forbes/Leadership, available at: www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-thingsthat-anyone-can-understand/ Oberman, E. (2014), “A lack of communication on cyber security will cost your business big (infographic)”, Entrepreneur, available at: www.entrepreneur.com/article/235318 Pera, M. (2014), “Libraries and the ‘internet of things’”, American Libraries, available at: http:// americanlibrariesmagazine.org/blogs/the-scoop/libraries-and-the-internet-of-things/ Rubenking, N. (2015), “Can we secure the internet of things?”, available at: www.pcmag.com/ article2/0,2817,2482620,00.asp
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Further reading Newby, G. (2002), In Kisielnicki, J. (Ed.), Information Security for Libraries, IGI Global, Pennsylvania. OCLC (2015), “Libraries and the internet of things”, Next Space, OCLC, available at: www.oclc. org/publications/nextspace/articles/issue24/librariesandtheinternetofthings.en.html Raine, L. (2014), “The internet of things and what it means for libraries”, Pew Research Center Internet Project, available at: www.pewinternet.org/2014/10/28/the-internet-of-things-andwhat-it-mean-for-librarians/ Steinberg, J. (2014), “Massive internet security vulnerability – here’s what you need to do”, Forbes, available at: www.forbes.com/sites/josephsteinberg/2014/04/10/massive-internet-securityvulnerability-you-are-at-risk-what-you-need-to-do/2/ Corresponding author Bruce Massis can be contacted at: [email protected]
For instructions on how to order reprints of this article, please visit our website: www.emeraldgrouppublishing.com/licensing/reprints.htm Or contact us for further details: [email protected]
THE ‘‘INTERNET OF THINGS’’: WHAT IT IS AND WHAT IT MEANS FOR LIBRARIES
Medical Reference Services Quarterly, 34(3):353–358, 2015 Published with license by Taylor & Francis ISSN: 0276-3869 print=1540-9597 online DOI: 10.1080/02763869.2015.1052699
EMERGING TECHNOLOGIES Matthew B. Hoy and Tara J. Brigham, Column Editors
The ‘‘Internet of Things’’: What It Is and What It Means for Libraries MATTHEW B. HOY Mayo Clinic Libraries, Mayo Health System–Eau Claire, Eau Claire, Wisconsin, USA
The ‘‘Internet of Things’’ is a popular buzzword but a poorly understood concept. In short, it refers to everyday objects that can sense the environment around them and communicate that data to other objects and services via the Internet. This column will briefly explain what the Internet of Things is and how it might be useful for libraries. It will also discuss some of the problems with and objections to this technology. A list of currently available Internet of Things examples is also included. KEYWORDS Intelligent objects, Internet, Internet of Things, IoT, libraries, pervasive computing
INTRODUCTION The idea of an ‘‘Internet of Things’’ (IoT) has been around almost as long as the Internet itself, but the concept is still poorly defined and even more poorly understood. The canonical example of an Internet of Things device is the ‘‘smart fridge’’ that knows when the milk has almost run out and adds it to the shopping list. But the reality of the IoT is much more complex and has many more possible uses (and misuses) than just keeping the refrigerator stocked. This column will attempt to explain what the IoT is, how it is # Matthew B. Hoy Comments and suggestions should be sent to the Column Editors: Matthew B. Hoy ([email protected]) and Tara J. Brigham ([email protected]). Address correspondence to Matthew B. Hoy, Mayo Clinic Libraries, Mayo Clinic Health System–Eau Claire, 1221 Whipple Street, Eau Claire, WI 54701. E-mail: [email protected] 353
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currently being used, and how it may develop in the future. It will also discuss some of the problems and objections being raised about this technology. Possible uses in libraries and a list of currently available IoT devices are also provided.
WHAT IS THE INTERNET OF THINGS The central idea of the IoT is that the Internet is no longer a place to visit, disconnected from the real world. Everyday objects are connected to each other and the broader Internet, allowing data to flow to and from everything. As Atzori et al. put it, the IoT is ‘‘a world-wide network of interconnected objects uniquely addressable, based on standard communication protocols.’’1 This idea of ‘‘smart objects’’ is older than the term ‘‘internet of things’’; it has been referred to in the past as ‘‘pervasive computing’’ or ‘‘ubiquitous computing.’’2 As hardware costs continue to drop, and wireless networks become more pervasive, it becomes trivial to connect everything. Estimates are that by 2020, there will be 50 billion devices connected to the Internet.3 As more interconnected objects come online, the possibilities for interaction increase exponentially. And as more and more people carry Internet-connected phones, fewer and fewer people are ever truly ‘‘offline.’’ In an IoT world, the air conditioner turns on at 4:30 instead of 5:00 because the car notified the house that you left work early.4 Your doctor is notified and your medical records are updated when your home medical lab notices a positive trend in your blood sugars. The washing machine automatically orders more detergent when the bottle is nearly empty, ensuring you never run out. Everything talks to everything else, and it happens wirelessly and with as little human intervention as possible. It is important to note that the term Internet of Things is actually referring to the combination of three distinct ideas: a large number of ‘‘smart’’ objects, all connected to the Internet, with applications and services using the data from these objects to create interactions. Or as Miorandi et al. succinctly described it, ‘‘the IoT builds on three pillars related to the ability of smart objects to: (i) be identifiable, (ii) to communicate and (iii) to interact.’’5 Kevin Ashton, who is widely credited with coining the term Internet of Things, notes that ‘‘if we had computers that knew everything there was to know about things—using data they gathered without any help from us—we would be able to track and count everything, and greatly reduce waste, loss and cost.’’6
PROBLEMS WITH THE INTERNET OF THINGS While there is no denying the wide array of possibilities offered by the IoT, there are also several problems. The main problem is one of privacy and
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security. When every connected object is passing data back and forth, there will be breaches and security concerns. As noted in a recent Federal Trade Commission report, IoT devices may present a variety of potential security risks that could be exploited to harm consumers by: (1) enabling unauthorized access and misuse of personal information; (2) facilitating attacks on other systems; and (3) creating safety risks.7
When the number of connected devices increases, so does the risk of unauthorized access and the opportunity for harm. The same report noted that an attacker was able to ‘‘hack remotely into two different connected insulin pumps and change their settings so that they no longer delivered medicine.’’7 When devices that users physically interact with can be accessed by malicious third parties, the possibilities for injury and mayhem are very real. Another issue facing the widespread adoption of the IoT is that of standards. Elgan is doubtful that the utopian vision of all these devices talking with each other will ever come to pass. As he says, ‘‘nowadays, the Internet is in the control of corporations, each with a vested interest in using standards to gain an advantage, lock out competitors and make profits.’’8 The marketplace for IoT devices is crowded, and at the moment there are no clear leaders mandating standards. Companies have no incentive to make their devices compatible with others and are creating their own branded devices that do specific tasks. The current state of the IoT is ‘‘like early websites circa 1995: separate ‘islands’ of information, providing specific data analysis for individual companies and industries.’’9 Until a clear standard for data exchange between ‘‘things’’ emerges, expect progress to be fragmented and slow. There may even be a more fundamental issue with the IoT: do we really want it? Brue Sterling, a noted author and futurist believes that there are inherent social problems with the IoT: The Internet of Things makes no attempt to redress, or even address, the many real problems that the internet brought to the world. On the contrary, it’s an international effort to bring everything that wasn’t Internet within the purview of the techno-elite that currently dominates the Internet.10
Sterling views the IoT as little more than a chance for mega-corporations such as Apple, Google, and Amazon to turn consumers into cogs in their automated product-consuming machines. Sterling’s concerns are not without merit; how can we hope to function in a world that is always on, always connected, and always knows just when to sell us something?
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LIBRARIES AND THE INTERNET OF THINGS Libraries are uniquely positioned to take advantage of Internet of Things technologies. Not only can many of these things be integrated into the physical space of the library, but librarians can act as local experts to help their patrons understand these new devices and integrate them into their lives. A recent survey conducted by OCLC found that many librarians had at least a passing familiarity with IoT technologies, and survey respondents listed several ways these new tools could be integrated into the library. Examples included: . . . . .
inventory control mobile payments, ticketing, and event registration climate and room configuration, accessibility, and way-finding mobile reference resource availability for both content and physical plant (rooms, AV equipment)9
One currently available IoT device that could be of interest to libraries is called a Beacon. A combination of a smartphone app and a transmitter beacon provide location-specific information and updates tailored to individual patrons. Libraries can use these beacons to provide event announcements, way-finding, and item recommendations.11,12 The IoT should also be of particular interest to medical librarians. Many of the devices currently in development are related to medicine, from assisted living activity sensors to smart pill bottles that remind patients when to take their medications.
CURRENT EXAMPLES OF INTERNET OF THINGS DEVICES The following devices are currently available and demonstrate the possibilities of IoT. These are only a few examples, and by the time this article reaches press, there will undoubtedly be more: .
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Chui, a web enabled doorbell that uses facial recognition to open the door for friends and family and notifies the owner via e-mail and video when a stranger rings the doorbell. Mimo, an IoT baby monitor that tracks baby’s body position, sleep status, breathing, and lets parents listen in, all via a smartphone app. GROUND Lab’s Open Source Lion Tracking Collars alert cattle herders in Southern Kenya via text message when a lion is nearby, allowing them to protect their herds without needing to kill the lions.
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GlowCaps, a medication reminder device that fits onto pill bottles. Using data entered in a web application, the caps provide audio and visual reminders for users to take their medications consistently. Medication compliance is tracked and recorded and can be shared with caregivers and physicians. The device also features an automated refill button and an additional reminder light. Bikn, a combination of a specialized phone case and GPS enabled tags that allow users to locate lost items, including the phone itself, even if the battery has died. Delphi connect, a device that plugs into a cars diagnostic port and allows the user to track the car, diagnose engine problems, and control many of the cars systems via a smartphone app. It can also provide a local wifi network inside the vehicle via a cellular modem.
CONCLUSION The Internet of Things is an exciting concept that has only begun to be implemented. As more ‘‘smart’’ devices are developed, there will be many new ways for users to interact with their environment and each other. Although there are currently no clear standards and no ‘‘killer app’’ for IoT yet, librarians should be aware that this technology is coming. There are several problems that need to be dealt with, including privacy and security concerns, but the Internet of Things could fundamentally change the way people interact with their environment, their devices, and each other.
REFERENCES 1. Atzori, Luigi, Antonio Iera, and Giaroma Morabito. ‘‘The Internet of Things: A Survey.’’ Computer Networks 54, no. 15 (2010): 2787–2805. doi:10.1016=j. comnet.2010.05.010. 2. Aggarwal, Charu C., Naveen Ashish, and Amit Sheth. ‘‘The Internet of Things: A Survey from the Data-Centric Perspective.’’ In Managing and Mining Sensor Data, edited by Charu C. Aggarwal, 383–420. New York: Springer Science & Business Media, 2013. 3. Itzkovtch, Avi. ‘‘The Internet of Things and the Mythical Smart Fridge.’’ UX Magazine. September 18, 2013. http://uxmag.com/articles/the-internet-ofthings-and-the-mythical-smart-fridge. 4. Motwani, R. ‘‘Leveraging the Internet of Things for Enhancing Learning Experiences.’’ INTED2013 Proceedings, 6207–6214. http://library.iated.org/view/ MOTWANI2013LEV. 5. Miorandi, Daniele, Sabrina Sicari, Francesco De Pellegrini, and Imrich Chlamtac. ‘‘Internet of Things: Vision, Applications and Research Challenges.’’
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Ad Hoc Networks 10, no. 7 (September 2012): 1497–1516. doi:10.1016=j.adhoc. 2012.02.016. 6. Ashton, Kevin. ‘‘That ‘Internet of Things’ Thing.’’ RFID Journal. June 22, 2009. http://www.rfidjournal.com/articles/view?4986. 7. Federal Trade Commission. ‘‘Internet of Things: Privacy and Security in a Connected World.’’ FTC.gov. January 2015. https://www.ftc.gov/system/files/ documents/reports/federal-trade-commission-staff-report-november-2013-workshopentitled-internet-things-privacy/150127iotrpt.pdf. 8. Elgan, Mike. ‘‘Why the Internet of Things May Never Happen (Part 2).’’ Computerworld. September 27, 2014. http://www.computerworld.com/article/ 2687741/why-the-internet-of-things-may-never-happen-part-2.html. 9. OCLC. ‘‘Libraries and the Internet of Things.’’ OCLC NEXTSpace. Febrauary 15, 2015. https://www.oclc.org/publications/nextspace/articles/issue24/librariesandthe internetofthings.en.html. 10. Sterling, Bruce. The Epic Struggle of the Internet of Things. Moscow: Strelka Press, 2014. 11. Mishra, Rit. ‘‘The Game-Changing Nature of Beacons.’’ UX Magazine. February 26, 2014. http://uxmag.com/articles/the-game-changing-nature-of-beacons. 12. Sarmah, Satta. ‘‘The Internet of Things Plan to Make Libraries and Museums Awesomer.’’ Fast Company. January 7, 2015. http://www.fastcompany. com/3040451/elasticity/the-internet-of-things-plan-to-make-libraries-and-museumsawesomer.
ABOUT THE AUTHOR Matthew B. Hoy, MLIS ([email protected]) is Supervisor, Mayo Clinic Libraries, Mayo Clinic Health System–Eau Claire, 1221 Whipple Street, Eau Claire, WI 54701.
THE SCIENTIFIC INFORMATION EXCHANGE GENERAL MODEL AT DIGITAL LIBRARY CONTEXT: INTERNET OF THINGS
University of Nebraska - Lincoln
DigitalCommons@University of Nebraska - Lincoln Library Philosophy and Practice (e-journal)
Libraries at University of Nebraska-Lincoln
Winter 1-30-2019
The Scientific Information Exchange General Model at Digital Library Context: Internet of Things Nayere Soleimanzade University of Isfahan
Asefeh Asemi Associate Professor in Department of Knowledge and Information Science, University of Isfahan, [email protected]
Mozafar CheshmehSohrabi Associate Professor in Department of Knowledge and Information Science, University of Isfahan
Ahmad Shabani Professor in Department of Knowledge and Information Science, University of Isfahan
Follow this and additional works at: http://digitalcommons.unl.edu/libphilprac Part of the Digital Communications and Networking Commons, and the Scholarly Communication Commons Soleimanzade, Nayere; Asemi, Asefeh; CheshmehSohrabi, Mozafar; and Shabani, Ahmad, "The Scientific Information Exchange General Model at Digital Library Context: Internet of Things" (2019). Library Philosophy and Practice (e-journal). 2150. http://digitalcommons.unl.edu/libphilprac/2150
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The Scientific Information Exchange General Model at Digital Library Context: Internet of Things Nayere Sadat Soleimanzade Najafi PhD Candidate Department of Knowledge and Information Science; Faculty of Education and Psychology; University of Isfahan; Isfahan; Iran [email protected]
Asefeh Asemi Department of Knowledge and Information Science; Faculty of Education and Psychology; University of Isfahan; Isfahan; Iran School of Business Informatics, University of Budapest, Budapest, Hungary Corresponding Author [email protected]
Mozafar CheshmehSohrabi Department of Knowledge and Information Science; Faculty of Education and Psychology; University of Isfahan; Isfahan; Iran [email protected]
Ahmad Shabani Department of Knowledge and Information Science; Faculty of Education and Psychology; University of Isfahan; Isfahan; Iran [email protected]
Abstract Introduction: This paper aims to develop a Scientific Information Exchange General Model at Digital Library in Context of Internet of things, which would enable automated and efficient library services. To accomplish its objective, the main classes (Concepts), sub-classes, attributes are identified in order to introduce an appropriate model. Methodology: The approach of this study is basic, exploratory, and developmental and is run through a mixed method consisting of documentary, Delphi, and data modeling methods. The study population in the documentary section includes the study of information resources retrieved in related subjects. The study population in the Delphi section is consist of 15 experts in “Internet of Things” and “digital library” domains. The Data gathering procedure is by applying a semi-structured interview. Appropriate software is applied for the analysis. Results: The findings showed that the 9 main classes of “End user”, “librarian”, “Microcomputer”, “Digital library server”, “Automated information services”, “Physical resources”, “Virtual resources”, “Information resources on the digital library server (virtual object)”, and “Security” in general model of scientific information exchange are very contributive. In general, 27 sub-classes and 38 attributes are identified for the main classes for this purpose. In this model, how the classes communicate and interact with one another is illustrated to justify this theme.
Conclusion: Here it is deduced that focusing on data protection at two levels of user and server in the main class of security is very important. Focusing on information resources metadata in the entity class, and device to device communication in this model is of essence as well. This proposed model is contributive in information networking in Internet of things-based library systems in providing better services to users. Research value: This model has potential in offering a basic proposal as a startup for automated library services. Keywords: Internet of Things, Scientific Information Exchange, Information Networking, Digital Library, Data Modeling, Data Model, Virtual Object
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Introduction The Internet of Things is a new intriguing phenomenon that has made many researchers involved in academics and industry context. Here, real-world physical and virtual things become connected to the Internet. Because of the heterogeneous and distributed nature of things, it is difficult for them to integrate and interact with one another because they add more things to the Internet (Raiwani, 2013). The main drawback of Internet of things (IoT) is lack of integrity in things' description, and adding another platform makes this drawback even more acute. Consequently focus is on providing of platform; an abstract layer that accepts existing platforms. Focus relies on rich modeling standards (Smith, 2004). Therefore, devising a basis and common language for systems and the architecture of IoT is a must. In this context, these models and standards need to be proposed and developed in various industrial and service enterprises and be adapted to their characteristics and requirements in a sense that in addition to incorporating the main concepts in the basic models of the IoT, like "existence", "resource", "service", and sometimes "device," other concepts are consistent within the specific requirements of the enterprises. Accordingly, this proposed model can be considered as the basis for the implementation of the Internet of objects in industry and service. For this purpose, digital libraries are no exception, where in order to take advantage of the benefits of information networking in the context of the IoT and their implementation, providing data models consistent with the service functions of these enterprises become essential. Scientific information exchange and information networking are among the important services provided through the digital libraries, where by adopting IoT becomes efficient and automated. Scientific information exchange is to facilitate communication among researchers at different points of digital library, and it constitutes the basis for scientific development. By adopting IoT in scientific information exchange system, the digital library facilitates access to scientific information, improves the quality of information exchange, and ultimately facilitates the exchange of data among digital libraries, the same true for users in an automated sense with no need for interpersonal involvement. The IoT can be regarded as an automated information network where all entities are capable of producing, transferring, and sharing their data. Applying such information networking in the digital library will result in automation, in addition to enhancing the quality of services. The first step here is identifying the main classes and sub classes and the basic features of the model to provide a model for the scientific information exchange at digital library in IoT context. This step Library Philosophy and Practice Jan 2019
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is followed by meeting the modeling techniques and methods' requirements. Data modeling (West, 2011) is one of these techniques for defining and analyzing data in information systems. The main restriction here is the absence of the manner in selecting the general model for scientific information exchange in a digital library through IoT. To provide this general model, the main and sub classes of the model should be identified. This necessitates the careful examination of the features of each one of these classes in the model; it is notable that this proposed model is not specific to a general or special digital library. In general, this proposed model provides the main concepts, sub-concepts, attributes and to a lesser extent, the correlations available in Scientific Information Exchange General Model at Digital Library in IoT context (SIEGMDLIoT). In order to accomplish this objective, the following specific questions are answered in this study: 1. What constitutes the main classes in SIEGMDLIoT? 2. What constitutes the sub classes in SIEGMDLIoT? 3. What constitutes the Features in SIEGMDLIoT? 4. What is a SIEGMDLIoT?
Literature review There exit many applicable metadata models and standards for integrating and facilitating the connection of physical and virtual objects to one another. In 2010, Kortuem et al, proposed a nonfunctional metadata model, where, the smart object is categorized based on either of the following dimensions: design, activity-awareness, policy- awareness, and process- awareness. Such a classification is in program -oriented domain towards the design of smart objects and could be applied in IoT systems' development. Such collaboration is not functional, because it could only classify smart objects based on design aspects. Kawsar et al (2010) proposed a functional metadata model, with the purpose to manage the smart object through Profile Description Document (PDD) (including information about the smart object's tools and capabilities). In their model, the functional classification of the smart object is based on the two documents of Smart Object Description Document (SODD) and the Profile Description Document (PDD). This categorization is assigned to the implementation and management of the smart object supported by the FedNet middle ware and the basis for creating discovery services and smart object management systems. In 2011, Uckelmann et al, proposed a non-functional metadata model, where the smart object is categorized according to the developer (self-made, ready-made) and purpose (specific and open). Only the two aspects of Library Philosophy and Practice Jan 2019
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(developer and objective) are considered in this classification and are not related to the cyberphysical features of smart objects. Therefore, such a classification cannot be applied in a functional manner in IoT systems. In 2011, Serbanati et al, proposed agnostic models of conceptual technology, as to extract smart object relations with the digital proxy Peer and its user by smart object model. In this model, the information of the smart object is available in the searchable resource registry. This model, with the concepts like accumulation of smart objects and the link between services and resources provides flexible guides for smart object modeling. In 2011, Pascual-Espada et al, introduced the resources managed by device programs model, which deals with the concepts that can be considered as virtual objects. In their model, the virtual objects consist of a numbers of records stored in a database. One of the drawbacks of this model in its ability in running virtual objects on different devices; of course these devices which want to display a virtual object should be equipped with a specific application that recognizes the specific format of the object. Another drawback, due to the previous one, is, if any program is only able to interpret a particular type of object, the devices need a large number of installed programs. In addition to the complexity, due to the development of an application for each type of virtual object, device and operating system, it would be inappropriate for each website to require a specific browser for interpretation. Pascual-Espada et al, (2011), proposed the model of resources managed by web applications, where the concepts that can be considered as virtual objects are of concern. In this model, virtual objects consist of records in the data warehouse, managed by web applications. In such systems, certain devices like automated teller machine (ATM), are directly linked to the management plan, are applied. Here, the object interpretation and management is not conditional on the client installed program because it is run through a web browser. This type of system is problematic when the virtual objects communicate with other applications outside, where they are run. There exit many web applications that provide APIs as web services for data stored in the Web application (model an object), which can be integrated applications. Although this alternative may be sufficient in some cases, this solution is far from ideal. There still exit problems with the connection of physical object and virtual object associated with the location-related services (supermarket, parking, etc.). Fortino et al (2013) proposed a metadata model for representing the functional and nonfunctional features of smart objects in a structured manner in order to index, discover, and select the dynamic smart object identified by the smart object model. The four main categories of the model include the type, device, service and location. This model is more general than the one proposed by Serbanati et al (2011) and Library Philosophy and Practice Jan 2019
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is implemented in the discovery framework for indexing smart objects, dynamic discovery and selection. The IoT reference model was presented by Bauer et al (2013), the development of the basis and common language for systems and the architecture of the IoT is the objective of this model. This model is made up of sub-models of domain, information, function, communications, trust, security and privacy. Fortino et al (2014) presented the smart metadata model. The purpose of this model is to describe a smart object in every area of interest (like smart city, factory, home, grid, building, etc.). The eight categories of this metadata model consist of: status, fingerprint, physical features, service, device, user, and location of the constituent concepts of this metadata model. This metadata model revealed that the static parameters of the smart objects, while showing no relevant dynamic parameters. These parameters can be restored through the operation of the available services or from the smart object position (usually through the smart object positioning service). Yachir et al (2016), provided a comprehensive semantic model for describing and applying smart object solution in the IoT. The concepts of person, space or place, equipment, device, and service constitute the main concepts considered in this model. They considered concepts like person, device or equipment as entity. In this study, the concepts of person, equipment and device is located in the concept of a specific place and the concept of the device is the service provider. The equipment includes the device and is controlled by the device. They measured the effectiveness and applicability of their semantic model as a case study for smart environmental monitoring. Summarizing the literature review In general, in a study where data and metadata models a proposal, architectural models, IoT reference models of, or virtual object models or physical object models develop models in general, and no specific industry or applications were considered There exit cases where special use is of concern (like development of a virtual object model for movie tickets, or smart environmental monitoring). In general, the count of fundamental studies that provide a SIEGMDLIoT, in order to provide smartness to the library is rare. Any model would applySIEGMDLIoT next to applying a software, hardware and middleware, could be one step in advance towards its being implemented in the library, thus the objective of this study. Method The method adopted here is basic, exploratory, and developmental. This study is run through a combined method consisting of documentary, Delphi, and data modeling. Here, the Delphi method is Library Philosophy and Practice Jan 2019
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applied to determine the main concepts, sub- concepts, and features of the general model regarding the theme, in order to implement the IoT in library systems. The documentary method is applied to prepare Delphi's first round interview form,
the basis of this study. The research community in the
documentary study consists of: study of information resources retrieved in IoT, Scientific Information Exchange, Information Networking, Digital Library, Data Model and Virtual and physical Object subjects. Sampling method in the documentary involves studying the related resources. The research community the Delphi panel consists of 15 experts who form the Delphi panel members in the “IoT” and “digital library” domains. The sample is selected in the Delphi qualitative section through Purposeful sampling. The data gathering tool is the semi-structured interview. According to the research objectives, SPSS software is applied in analyzing the results of three rounds of run Delphi process. After reviewing the information resources and extracting the main concepts, sub concepts and attributes, the first Delphi interview form is designed. The members of the Delphi panel came up with consensus and finalization in relation to the main concepts, sub concepts, and attributes in three phases. By applying a data modeling method, a basic model is devised for the scientific information exchange. Data modeling is the process of devising a data model for an information system by applying specific formal techniques. This data model provides a framework for data, applicable in information systems by providing a specific definition and format"(Simsion & Witt, 2004). By applying Protégé's software and data modeling method, this model is designed. Results The obtained findings here are based on research questions in the following four sections containing: the main concepts, sub-concepts, attributes/features, and SIEGMDLIoT. The main classes in a Scientific Information Exchange General Model at Digital Library in context of Internet of things The 9 main concepts regarding SIEGMDLIoT are tabulated in table 1, where the position of each concept is determined in the IoT architecture layers.
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Figure 1. Main classes in a Scientific Information Exchange General Model
As observed in figure 1, the main 9 classes of: End-user, librarians, microcomputer, digital library servers, automated information services, physical resources, virtual resources, information resources on digital library server, and security are the main concepts regarding this them. Table 1. Main classes in a Scientific Information Exchange General Model General main concepts User
General main concepts defined in digital library End User Librarian Microcomputers
Device Digital Library webserver Service Resource Entity Security
Automated Information Services Physical Resources Digital Resources Information Resources on Digital Library web server Security
Position of each concept in the IoT architecture layers Management Layer Management Layer; Application Layer; Network Layer Management Layer; Network Layer Management Layer; Network Layer; Service Layer; Final application Layer Management Layer Management Layer Management Layer
As observed in table 1, all concepts are located in the management layer, the device (microcomputer), the digital library server and the automated information services are located on the network layer, the microcomputer is in the application layer, and the automated information services are in the service and the final application layer.
The sub classes in a Scientific Information Exchange General Model at Digital Library in context of IoT The results obtained from the views and comments of the members of the Delphi Panel on subclasses are tabulated in table 2 and figure 2. Library Philosophy and Practice Jan 2019
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Sub concept Normal users Users with specific features Users with specific needs -
End User Librarian
Desktop Computer Microcomputers Digital Library Server Automated Information Services
Physical Resources
Virtual Resources
Laptop - Notebook Computer PDA Information storage and increasing server power and speed devices Authentication and user profile control automated Data retrieval Automated Data access Automated Data sharing Memory CPU Network bandwidth Energy Used Device Data Repository Security Reid system plugin Access permissions Copyright Platforms that fit different groups of users UI graphical features
Information Resources on Digital Library web server
Book All non-book document Microcomputers-level security Server-level security
Security
Figure 2. Classes in a Scientific Information Exchange General Model
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Features in a Scientific Information Exchange General Model at Digital Library in context of Internet of things Table 3 observed the attributes defined for the main and sub classes. For each of the main concepts, it can be considered depending on the need for some features. This table lists some of the most important features. Table 3. Features in a Scientific Information Exchange General Model main concepts
End User
User Profile
Librarian
Microcomputers/ Digital Library Server
Automated Information Services
Physical Resources/ virtual Resources
Information Resources on Digital Library web server
Security
sub concept Name Last Name Id Number Sex Age Degree(Field of Study, Grade ) Job Research and study interests How to contact the library (contact number, email address, telegram, etc.) Type of document required (book, article, film, conference and seminar, audio file, government report, patent, laws and regulations, other information sources) Information resources used by the user (Information resources and organizations referenced) Device Profile Status Identifier Type Service Quality Creator Name Type Service Quality Authentication factors Time and Location Features Value Access Mechanism(library is the owner of Information Resource or not) Free or cost-effective Name Type Time and Location Features External metadata and link to them Name File Type Identifier External metadata and their links Information source specifications and Summary Accuracy Privacy Access control Authentication
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Scientific Information Exchange General Model at Digital Library in context of IoT To design SIEGMDLIoT, based on the main concepts, sub-concepts and extracted attributes, and their relationships, applying the Protégé 5.2 software, which is a tool that can devise ontology and define classes, data properties, objects, instances, relationships, etc. (Tudorache et al, 2013). The reason for applying this software is to allow the model clearly illustrate the main concepts, subconcepts, attributes and relationships, figure 3 shows some relationships include accesses, device-todevice communication, are attached to, are part of, Manage process, use device application on, has container, and has function.
Figure 3. Scientific Information Exchange General Model at Digital Library in context of IoT
A simple schema of the Scientific Information Exchange General Model at Digital Library in context of IoT is shown in figure 4.
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Figure 4. Scientific Information Exchange General Model at Digital Library in context of IoT
Model Interpretation In general, the scientific information exchange at digital library in context of IoT begins with the activity of digital librarian in three parts of: 1)librarian management in preparing and delivering information resources to the user, 2)managing information resources security issues and 3)and controlling dissemination and access in accordance with copyright laws, and managing and consulting on the structure and requirements of the user interface of the application used in the user’s microcomputer. The first two parts are on the digital library server and the third is on the microcomputer. In the next step, the digital library server is considered as a device in the IoT topic. The server has a function called are named provision of information services, possible in the network context. At the two ends of this network, there exist the two devices of server and microcomputer; connected to each other, based on IoT communications protocols, therefore, it is possible to provide automated services. The information service is the next concept, for which there must be access among this concept and the physical and virtual resource concept. The most basic physical resource is the network where scientific information exchange is within its context. The access and relation of service with resources like memory, network bandwidth, security, Raid system, copyright policy, platforms, and graphical user interface features are of efficiency. Physical and virtual resources with virtual entity or information resources on the digital library server are in contact as well. Information resources are connected to the device. Here, it can be claimed that the device (server and
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microcomputer) acts as a container for information resources. The end user can access the information resources with the assistance of his microcomputer application. Discussion and Conclusion Majority of the members of the Delphi panel believe that the 9 concepts involved here constitute the essential elements in materializing this theme. In this study, the entity is considered as a virtual thing (virtual information resources); In addition to research focused on physical entities; it can also be compared with research that considers the virtual things as an entity. In most studies, the three main concepts of entity, service and resource are considered as the main concepts of the service model in the context of the IoT; while, in some, the necessity of the device is referred to as one of the main concept. In this context, De et al (2011) referred to the four entity, resource, device and service as the main concepts of the information model of the IoT. In a study run on Service Modeling for the IoT; and in the OWLS service ontology, for the domain model, the three main concepts of entity, resource, and service are considered as the main concepts. To Pascual-Espada et al (2011) the concepts of entity ((virtual object) such as cinema tickets), resource, device, and service are considered as the main concepts. To Fornito et al (2013) type, device, service, and location are considered as the main concepts. Fortino et al (2014), considered the eight concepts of status, fingerprints, physical characteristics, service, device, user and location are considered as the main concepts together with a physical library for model testing. Yachir et al (2016) considered the concepts of person, space or place, equipment, device and service are as the main concepts. In their study, they considered concepts such as person, device or equipment as entity. In this study, the concepts of person, equipment and device are in the concept of a specific place and the concept of the device is the service provider. The device is devised by a person. The device is in the equipment and controlled by the device. They measured the effectiveness and applicability of their semantic model as a case study for intelligent environmental monitoring. Based on the obtained results, each one of the 9 main concepts includes sub-concepts in the scientific information exchange model in the IoT context. The normal users, users with specific features and users with specific needs are considered as part of the user's sub-concepts in this model. The need to consider these sub concepts for the end user concept is due to the change of user interface, hardware and software requirements different from those of each user group. These features of the user must be specified in the user profile. Consequently, it is possible to provide automatic service to the user according to his or her conditions. It should be noted that one of the target groups in the IoT is the disabled. In this context, Library Philosophy and Practice Jan 2019
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by living and working in intelligent environments, this group of individuals if resort to IoT will be able to independently carry out their daily activities. The field of scientific information exchange and seeks information and isno exception, that is, these individuals will be able to independently and with providing their profile to information systems in the context of the IoT with sharing information in an easy manner. The other category of sub-concepts in this model is related to the device. Devices like Desktop Computer, Laptop - Notebook Computer, PDA on one hand, and data storage and increasing the server's power and speed devices, on the other, can be considered among the device's sub concepts. The need to consider the device applied by the user as a sub concept, is the adaptation of the program and software used in the IoT in terms of user interface, software volume, etc. with the user's device. To illustrate this, for example, whether in the IoT system of the library, software designed and available for the intelligent object and the IoT like openHAB and their extension, or from software designed in the field of libraries like caliber and expansion They are applied in accordance with smart object models and the IoT; The ability to load and run such software on any device used by the user is of major concern. Focusing on to storage devices as a sub concept of a server is essential in IoT system’s response speed as to the automated service. The count and type of server storage devices are one of the factors that increase the speed. Other categories of sub concepts in the model are service-related. One of the basic objectives of IoT systems is to provide timely, efficient and automatic service. Where applying scientific information exchange system on the context of IoT sub services like automated authentication and control of user's profiles, retrieval, access and sharing are based on user profiles. After verification and authentication, users can share their electronic resources available on their microcomputers with their academic colleagues through Bluetooth, WiFi or other protocols, where the function in two modes of notification and execution are justified. This function has an input, (i.e., identifier of an information resource) and an output (i.e. an existing resource of information). In this process, automatic search is carried out in the information resource database by applying the identifier. The process is such that at first the automatic search in the information resources database is run through the identifier. If an information resource is found, the user will be notified (who would download it if needed) or download the information resource directly. When that there is no information resource a correct answer is sent to the user. The other sub concept categories are related to the physical and virtual resources. The most basic physical resource is the network handling scientific information exchange in its context. Access and relation of service with other resources like memory, network bandwidth, security, Raid system, copyright policy, Library Philosophy and Practice Jan 2019
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platforms, and graphical user interface features will be of service efficiency. Plugin, access permissions Copyright, Platforms that fit different groups of users and UI graphical features constitute the virtual resources sub-concepts. The other groups of sub-concepts are related to entity. Which include the book and all non-book documents. Here the virtual object or entity is an electronic information resource that its owner of which is changed, exchanged, stored in the library server, retrieved and transferred. The other group is the sub concepts of security and data protection at the microcomputer level and the digital library server. In general, data protection is divided into two levels of information resources and user information protection. This security must be applied to, virtual entity, resource, and service layers of the device. At the information resource protection level , all the data protection on the server (device)), providing backup files, using security permissions like file level and sharing levels, protecting files and documents using a password and protecting data in transit (entity)), secure transmission in local, global, Internet and intranet, network access security, data security (network-level data flow), host security, application security (resource), Identification, authentication, encrypting the digital library server, cloud computing, social networking etc., retrieval, delivery (server / user database) (according to Copyright law, demographic information, etc.), access control (service) must be applied. At this level all the identification, licensing, encryption of the digital library server, cloud computing and social networking etc.; retrieval, delivery (server / user- database) (based on copyright law, demographic information, etc., control Access (service); data security (network-level data flow)(resource); secure application (device); authentication and control of user profiles; sharing (user-user) (archiving policy, etc.) (Service) must be applied. For each one of the main concepts, upon need, some features must be of concern. The user is introduced and known using the defined attributes for the system. A profile is developed for the user by attributes. The concept of a profile in the Scientific Information Exchange General Model at Digital Library in IoT is essential, because an important part of automation is based on the data and values available in the profile. Here the system recognizes its information needs by checking the user profile and performs the appropriate function on the appropriate information resource thereof. The microcomputer and the library server should have a well described definite time and space setting. Each device needs an identifier in order to be identified in the system. Each device has a special type of creator that is considered to be the key to describing that device. The service quality parameter describes the quality of service provided by the device. The concept of service also has features. As to access permission factors, it should be noted that it is an important feature of a service. Because Library Philosophy and Practice Jan 2019
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the levels of access to the service must be specified for different users the information contained in the system is protected. Given that the library is the owner of the information resource whether served or not, there exist different access mechanisms. Whether the information resource is in a form of a full text or a summary and abstract, is relevant to this feature. As to resource attributes, it should be noted that resources in IoT model should be described in order to determine, whether they are networks, storage spaces etc. One of the attributes for the entity concept is the file type, including: PDF, Epub, Zipped / unzipped, HTML, Mobipocket, Microsoft Office Word Doc / DocX, Microsoft Compiled HTML / CHM files, Plain text files, also known as ASCII text files. External metadata and the linkage thereof refers to the metadata needed to describe the information resources and the ontologies needed to describe some of the specific features that should be added to the system. In order to explain the proposed model based on the IoT reference model, by Bauer et al (2013), the abstract sub-models including domain, information, communication, functionality, and security for proposed model must be of concern. As to the sub-model of the domain in the Scientific Information Exchange General Model at Digital Library in IoT context, the basic concepts of the end-user, the librarian, the microcomputer, the digital library server, the automated information services, the physical resources, the virtual resources, the information resources on the digital library server and the security are considered. In the proposed model, these concepts are linked together in a chained manner. By incorporating information resources and user profiles the library server is a function named information service. This service is based on microcomputer; that is, receiving service in an automated manner which would allow the user to make some decisions about the service. For example, if a notification is sent to microcomputer application user about downloading an information resource in accordance with his/her profile and information needs, he/ she can decide whether or not to download it. Or if it is downloaded in one step and there is no notifications he/she; can decide on reading or sending and sharing it with colleagues or others. This process is forstoring the information object on the server, protecting it, sending and providing service in the context of the network and receiving it by a microcomputer, depending on resources. In this process, a set of physical and virtual resources are contributive. These resources on the server include memory, CPU, data storage devices, energy, copyrights and permissions access, Raid systems etc., upon need and in microcomputers include memory and platforms that fit different groups of users, graphical user interface features etc., upon need. The service process includes resources like network bandwidth, plug-ins, processes required for service, and more dependency on the need. In this model, what is Library Philosophy and Practice Jan 2019
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important about the target entity or information object or information resource is its connection to the specific metadata required by the entity. In this model, an entity is an information resource, thus, it is necessary for it to be linked to one of the Dublin Core, z39.50 metadata and the like, according to the metadata system of the information resources in the digital library. It should be noted that these metadata are considered as complementary metadata essential in Scientific Information Exchange General Model at Digital Library in IoT context. In addition to metadata, it may be necessary to apply different ontologies to describe different features of the main and sub-concepts. In this model, data protection in two parts is intended including of the server to protect information resources and in some extent user information protection and in the microcomputer to protect information of user profiles In general, in IoT, the debate on security is an important topic, specially here, where the objective of focus is on information resources and libraries. Because in relation to information resources, and especially electronic information sources, the copyright debate and copyright protection and control of user access levels, the monitoring of access mechanisms (may be the library is the information resource or not), the cost of receiving the work (which is in The model of the IoT can be considered as an attribute called value), is fundamental issues. In general, object communication protocols, in the IoT are divided into three categories of devices to devices, devices to cloud and devices to gateway. In this study, the communication protocol is device to device. In this model, the two devices of digital library server and user device are combined to each other to provide automated information services. In this study, the two layers of management and application prevail. The application layer refers to an application where the user can apply in the context of IoT to get the necessary services. The management layer is throughout the current process. Perhaps the two layers of management and application can be mentioned as the main concepts in this model, classes of which would be defined subsequently. This model can be a basis for future studies in this context. Given that this model is based on the services of the digital library, has a general view, researches with a Minority view, the IoT Model for each activity of library at two levels of information resources and physical and virtual equipment. The implementation of this model be clarified as an applied application of defects and its weaknesses. References Bauer M, Bui N, Loof JDe, Magerkurth C, Nettsträter A, Stefa J, Joachim WW. IoT Reference Model in Book: Enabling things to talk. Editors: Ovidiu Vermesan, Peter Fries. Chapter 7. Library Philosophy and Practice Jan 2019
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Denmark: Alborg; River Publishers Series in Communications. 2013. DOI: 10.1007/978-3642-40403-0_7 West M, Developing High Quality Data Models. USA: San Francisco, Morgan Kaufmann Publishers Inc. 2011. De S, Barnaghi P, Bauer M, Meissner S. Service Modelling for the Internet of Things. Proceedings of the Federated Conference on Computer Science and Information Systems. Poland: Szczecin, 2011; 18-21. Available at: https://ieeexplore.ieee.org/document/# Fortino G, Lackovic M, Russo W. Trunfio P. A discovery service for smart objects over an agentbased middleware. In: Pathan M., Wei G., Fortino G. (eds) Internet and Distributed Computing Systems. IDCS 2013, LNCS 8223. Springer, Berlin, Heidelberg: 281-293. doi: 10.1007/978-3-642-41428-2_23 Fortino G, Rovella A, Russo W, Savaglio C. On the classification of cyberphysical smart objects in internet of things. International Workshop on Networks of Cooperating Objects for Smart Cities. Germany, Berlin: UBICITEC 2014; 1156: 76–84. Simsion GC, Witt GC. Data Modeling Essentials. 3rd edition. USA: San Francisco, Morgan Kaufmann Publishers Inc. 2004. Pascual-Espada J., Sanjuan-Martinez O, Pelayo G-Bustelo C, Cueva-Lovelle JM. Virtual objects on the internet of things. International Journal of Interactive Multimedia and Artificial Intelligence 2011; 1(4): 23-29. Kawsar F, Nakajima T, Park JH, Yeo SS. Design and implementation of a framework for building distributed smart object systems. Journal of Supercomput 2010; 54(1): 4–28. doi: 10.1007/s11227-009-0323-4 Kortuem G, Kawsar F, Sundramoorthy V, Fitton D. Smart objects as building blocks for the Internet of things. IEEE Internet Computing 2010; 14(1): 44-51. Doi: 10.1109/MIC.2009.143 Raiwani YP. The Internet of Things: a new paradigm. International Journal of Scientific and Research Publications 2013; 3(4): 1-4. Available at: http://www.ijsrp.org/researchpaper-0413/ijsrp-p1656.pdf Smith R. RFID: A Brief Technology Analysis. CTOnet.org 2004. Available at: https://image.slidesharecdn.com/rfid-a-brief-technology-analysis1295/95/rfid-a-brieftechnology-analysis-1-728.jpg?cb=1275014396 Serbanati A, Medaglia CM, Ceipidor UB. Building Blocks of the Internet of Things: State of the Art and Beyond, Deploying RFID, Cristina Turcu, IntechOpen, 2011. doi: 10.5772/19997. Available from: https://www.intechopen.com/books/deploying-rfid-challenges-solutionsand-open-issues/building-blocks-of-the-internet-of-things-state-of-the-art-and-beyond Tudorache T, Nyulas C, Noy NF, Musen MA. WebProtégé: A Collaborative Ontology Editor and Knowledge Acquisition Tool for the Web. Semantic Web. 2013; 4(1):89-99. doi: 10.3233/SW-2012-0057
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Uckelmann D, Harrison M, Michahelles F. Architecting the Internet of Things. Berlin: Springer, 2011. Available at: https://pdfs.semanticscholar.org/85ae/6d8fd73beeeea1a0b8372b5af13ef8f2a105.pdf Yachir A, Djamaa B, Mecheti A, Amirat Y, Aissani M. A Comprehensive Semantic Model for Smart Object Description and Request Resolution in the Internet of Things. Procedia Computer Science 2016. 83: 147-154. doi: 10.1016/j.procs.2016.04.110. Available at: http://www.sciencedirect.com/science/article/pii/S1877050916301338
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THINGSPEAK BASED MONITORING IOT SYSTEM FOR COUNTING PEOPLE IN A LIBRARY
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/330765520
Thingspeak Based Monitoring IoT System for Counting People in A Library Conference Paper · January 2019 DOI: 10.1109/IDAP.2018.8620793
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Thingspeak Based Monitoring IoT System for Counting People in A Library Kadir Sabancı Department of Electrical and Electronic Engineering Karamanoğlu Mehmetbet University Karaman, Turkey [email protected]
Enes Yigit Department of Electrical and Electronic Engineering Karamanoğlu Mehmetbet University Karaman, Turkey [email protected]
Deniz Üstün Department of Computer Engineering Karamanoğlu Mehmetbet University Karaman, Turkey [email protected]
AbdurrahimToktaş Department of Electrical and Electronic Engineering Karamanoğlu Mehmetbet University Karaman, Turkey [email protected]
Yunus Çelik Department of Electrical and Electronic Engineering Karamanoğlu Mehmetbet University Karaman, Turkey [email protected] Abstract— In this study, a real-time system which counts the number of people with the help of a camera was demonstrated. The system can send the number of people to a mobile application via Internet of Things (IoT) and monitor simultaneously. This work is carried out in the main library of Inonu University. Background subtraction method was used to recognize moving humans on the visual field of the camera. According to motion information of humans, a counter was used to count the number of people in the saloon by determining whether going inside or outside. The counter will inform the users about what percentage of the saloon is empty. Matlab and Thingspeak combination help to send counter information to internet environment. A mobile application was used to track the counter information from Android and iOS smartphones. The results were presented in Matlab environment and mobile application simultaneously. Thanks to this work, students will not have to look for a place to work when the library is crowded and will not bother other working students. It is believed that this project will serve all students. Index Terms—Human application.
Detection,
Thingspeak,
Android
I. INTRODUCTION Computers are irreplaceable components of our daily lives for years. Their adjustable and programmable structures make them crucial for the industry to medical applications. Recently, computer-human interaction has gained a large amount of interest. These combination systems are quite smart and work efficiently in most areas such as security systems. 978-1-5386-6878-8/18/$31.00 ©2018 IEEE
Most of the computer-human interaction systems work with sensors and cameras to get the information to process. This paper focuses on the system which works with a camera. Most of the computer-human interaction systems with the camera need image processing stages according to requirements. Image processing is another profession with a large amount of interest in areas such as medical purposes, surveillance systems, and security systems. Image processing has lots of advantage for complicated systems [1-3]. Image processing algorithms are quite applicable for the systems with a large number of inputs and outputs. Human eyes can recognize objects and other items according to their color and shape, however this is not so easy for computers. Computer-human interaction with a camera mostly uses object recognition. Object recognition is one of the most important parts of image processing. Computers recognize objects to start the process. There are plenty of different methods to recognize objects in literature [4-5]. Gomez and friends created a monitoring system similar to this work but have more inputs. Sensor information were sent via IoT and also in case of emergency, user can warn the system or set an alarm to avoid from the dangerous situations [6]. One of them is background subtraction which eliminates background from the current frame to point out the foreground. This is not an easy task for real-time systems in dynamic environments. Tracking moving object on the camera is done by tracking moving pixels. This process works for cases where background pixels are static and foreground pixels are dynamic. Many filters can be used to improve process quality. Although this system has many beneficial aspects, there are disadvantages like counting shadows as an object and being sensitive according to the light amount in the environment.
Shadow detection and updating background frame can be good solutions to overcome disadvantages. Human detection from moving pixels is the most important phase of the system. According to the direction of moving pixels, the system determines the direction of human and count the total number of people in a saloon. Internet of Things (IoT) is a service to transfer data among the devices which has permission to access the same channel. IoT helps computers, laptops, tablets and smartphones to communicate simultaneously via the internet. This communication is mainly based sending information to a channel and monitoring information from computers, tablets, and smartphones. This new technology is being active in business and industrial applications day by day. Thingspeak is an internet based open application programming interface (API) IoT source information platform which has a quite wide range to store sensor data [3-5]. One of the most important terms of Thingspeak is a channel where the data are stored. This channel is also programmable and one can set limits to channel, if the value exceeds the limits, the channel can be programmed to twit or call someone [1,2,7,8]. In this paper, it is aimed to solve a problem for students who cannot find a place to study especially during exam weeks. The necessity of this project was discussed with students and it is believed to fix their problem. This project is applied to the saloons left and right located on the first floor. It is quite easy to apply each flat and combine the flats together. An Autonomous system was designed to count the number of people inside of a saloon. At the same time, the number of people can be monitored from smartphones, tablets or computers. It is also clear that this system can be applied to many areas which require sensor data transfer. II. BACKGROUND SUBTRACTION METHOD Motion Detection is a very common way to track humans and objects. Motion Detection can be managed by different methods such as Temporal difference, Optical flow, Background Subtraction. Each method has advantages over other methods. In our real-time system, the background frame is static so it is appropriate to use background subtraction to track and count humans. Motion Detection is necessary to understand human behaviour and directions. Background Subtraction method is a method which looks for the difference between background frame and a current frame [9-12]. This method is more powerful when it initializes and update background image. Outdoor or indoor places have unstable light amounts. This affect image processing stages directly. Light amount is one of the most important parameters to process images. During the day, this light amount changes. Background update is the key phase to overcome light amounts [15,16,19]. The flow chart of the systems is in Fig.1. As it can be seen clearly, in case of no human in the current frame, background update itself to adapt changing environment condition.
Input Real-Time Video
Image Sequence
Background Image Initialization
Background Update
Current Image
Background Subtraction
No Moving Object Extraction Yes Removal Noise and Eliminating Small Pixels
Computing Central Point of Object
Determining Motion Direction and Counting Fig.1. Flow chart of human tracking and counting algorithm A. Background Image Initialization Background image initialization is the first step of background update. There are some methods to get the initial image in literature [15]. These can be listed as making first frame background image directly, an average of first couple frame and background image sequence. In this project, we choose the first frame as the background image. Other methods are more powerful than this method but that will not be a big problem because of background updating [15-17]. B. Background Update The accuracy of human detection is heavily depending on updating background because of the changes in light amount in the environment. In every half second, we get a new frame from the camera and compare with the old frame. During the days, amount of light is changing in environments so pixel
values too. If there is no moving object in the current frame, we update the background image with its new values [15,17,18]. C. Moving Object Extraction At this stage, background image is subtracted from the current image. If the pixel difference is more than threshold T, then determines that the pixels appear in the moving objects. If not, pixels are background pixels.
1 , Fk ( x , y ) − B k −1 ( x , y ) > T Dk ( x, y ) = (1) 0 , others Where Dk ( x, y ) is the binary image of differential results, Fk ( x, y ) is current frame and Bk −1 ( x, y ) is background
E. Computation of Central Point of the Object The system gets a snapshot in every 20 milliseconds to process in real-time systems. The algorithm performs the whole process according to these snapshot images. After the detection of human in the frame, it is now to compute the central point of the human. This computation tells us about the direction of humans. Every image has 960x720 pixels resolution. That means we have 960 pixels vertical and 720 pixels horizontal. 960x720 is actually 16x9 aspect ratio just like 1280x720 pixels. The only difference is because of nonsquare pixels. The centre coordinates of the object were computed in x and y-axis [10,11,20]. Compute the zero moment
image. T is threshold rate determined manually by looking experimental results [15,19].
M 00 I ( x, y )
D. Removal Noise and Eliminating Small Pixels In our real system, background subtraction method always includes some noises which can be caused by the environment or light amount [15]. That’s why removing noises is the necessity to have a better process. During the process, there might be some other motion which is not derived from human motions. This situation creates some small pixels which must be eliminated to see human motion clearly. For that purpose, the threshold to determine background model was selected manually. Fig.2 shows all noise and a small pixel in the process. After usage of the median filter and selecting threshold rate, Figure.3 shows the human motions only. This stage has a great importance over the process because It is quite difficult to understand human motion from noisy image [15,16,19]. Thanks to the “bwareaopen” command it is possible to remove the pixels which is less than determined rate. Because of the fact that the object moves, there will be always some noisy pixels. It is shown in Figure.2. The effect of “bwareopen” command can be seen in Figure.3.
First moment values of x, y;
Fig.2. Human motion with noise
x
(2)
y
M 10 xI ( x, y )
(3)
M 01 yI ( x, y )
(4)
x
y
x
y
Localization values [20];
Xc =
M 10 M 01 , Yc = M 00 M 00
Performance of this section is depending on the quality of the camera and processor speed. Objects can be detected easier in high-resolution images than low-resolution image but the process speed is the opposite [20]. III. THINGSPEAK-IOT In recent years, controlling and monitoring sensors and other parameters has interest many engineers. This is because of the latest advance in a mobile phone. One can control a lamp from thousand kilometres by using IoT. This remote-control tool works through internet and acts as a data packet carrier between host microcontrollers like Arduino-Raspberry-pi and Thingspeak cloud [3,6,8]. This connection control can be selected as private or public. That means in case of the allowance, everyone can control a lamp at the same time. As it is underlined above, Thingspeak is a web-based open API IoT source information platform [3]. Users must have their own channels to send or monitor information. Creating own channel is totally free now. It is possible to monitor the locations and other visualizations in the channel [1,7].
Fig.3. Human motion without noise Fig.4. Channel location
Location sending is one of the most popular features of Thingspeak. It is possible to send location information to the cloud in every 15 seconds. There are many applications for tracking systems which are made from Arduino microcontroller, Esp8266 Wi-Fi module, and GSM/GPRS module [4-6]. In this work, Matlab-ThingSpeak Support Toolbox was used to write information to the cloud. The counter starts to count the number of people in a flat and sent it to cloud in every 15 seconds. We created 4 channels for this work. 2 channels have the information of the number of people and other 2 channels have the information about what percentage of the flat is empty.
according to A, B and C points. The distance between the current position of human and A, B and C points are compared to each other. The nearest point was taken into account. Table.1 shows the coordinate of A, B and C points in x and yaxis. These values were selected manually by the programmer. Table.1. A, B and C coordinates Name of Saloons
X-axis
Y-axis
A
200
400
B
900
400
C
490
700
Figure.6 shows 2 scenarios that a human enters to saloon A in scenario 1 and a human entering to saloon B in scenario 2.
IV. IMPLEMENTATION The system consists of a camera and a computer with internet access. The system can be implemented almost any area because of its cheap and easy structure. The implementation was successfully done in the first floor of Inonu University Central Library. Camera
System
Internet
Mobile Phone
Tablet
Fig.7. Scenario 1 & 2 Computer
Fig.5. Block diagram of system design First of all, the coordinates of saloons are determined in background image.
Figure.8 and Figure.9 shows the number of people and occupancy rate for A and B saloons on the computer. These records are gathered from 14:48 to 14:52 in 18.03.2018. Records are uploaded to the internet and monitored from Thingspeak website. The occupancy rate was calculated according to the capacity of Saloon which is 40 persons for each saloon.
Fig.8. Saloon. A. information Fig.6. Saloons in the library Fig.6 shows the locations of the saloons. There are 3 saloons named A, B and C. When the central point of human is coming closer to the A point, it means the human enter to saloon A and counter is +1. When the central point of a human going away from A point, it means the human go out from salon A and counter is - 1. This logic applied to all salons
Fig.9. Saloon. B. information
Figure.10 and Figure 11. shows the same graph from iOS mobile phone by using Thingview Application.
[6]
[7]
[8]
Fig.10. Saloon. A. information [9]
Fig.11. Saloon. B information V. CONCLUSION In this work, a human-computer interaction work was presented. A human counter system was designed in Matlab programming and applied to the main hall of a library. Additionally, the counter value was uploaded and monitored by a computer and a mobile phone via IoT and ThingSpeak interaction. It was aimed to explain the work with two simple scenarios. It has been seen that system is working with high accuracy even if there are some weak points like counting human shadow as a human. Another human recognition algorithm like face detection can be combined to improve the accuracy of the systems. It is believed that this work is open for further works and serve students. REFERENCES [1] S. Dimitris, S. Evaggelos, S. Giorgos and G. Theodoros. “Counting and Tracking People in a Smart Room,” 2015 10th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). [2] S. Iker, S. Javier Del, E. Inaki, V. Manuel.” Device-Free People Counting in IoT Environments: New Insights, Results and Open Challenges,” IEEE Internet of Things Journal 2018. [3] Pasha. S. “Thingspeak Based Sensing and Monitoring System for IoT with Matlab Analysis,” International Journal of New Technology and Research (IJNTR) ISSN: 2454-4116, Volume2, Issue-6, June 2016 Pages 19-23. [4] Prathibha. S, H. Anupama, M. Jyothi.” IOT BASED MONITORING SYSTEM IN SMART AGRICULTURE,” 2017 International Conference on Recent Advances in Electronics and Communication Technology. [5] Bassoli. M, Bianchi. V, Munari. I, and Ciampolini. P.” An IoT Approach for an AAL Wi-Fi-Based Monitoring System,” IEEE
Transactions on Instrumentation and Measurement, Vol. 66, No. 12, December 2017. Gómeza. J, Oviedob. B, Zhumab. Emilio.” Patient Monitoring System Based on Internet of Things,” The 7th International Conference on Ambient Systems, Networks and Technologies (ANT 2016). Akkaş. M, Sokullu. Radosveta.” An IoT-based greenhouse monitoring system with Micaz motes,” International Workshop on IoT, M2M and Healthcare (IMH 2017). Abdelgawad. A, Yelamarthi. Kumar and Khattab. Ahmed. “IoTBased Health Monitoring System for Active and Assisted Living,” Smart Object and Technologyies for Social Good Second International Conference, GOODTECHS 2016, Venice, Italy, November 30-December 1. Rao. B, Rao. K, Ome. N.” Internet of Things (IOT) Based Weather Monitoring system,” International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 5, Issue 9, September 2016.
[10] Yang. D, Banos. H, Guibas. L. “Counting People in Crowds with a Real-Time Network of Simple Image Sensors,” Proceedings Ninth IEEE International Conference on Computer Vision.13-16 Oct. 2003. [11] Subburaman. V, Descamps. A, Carincotte. C. “Counting people in the crowd using a generic head detector,” 2012 IEEE Ninth International Conference on Advanced Video and Signal-Based Surveillance 18-21 Sept. 2012 [12] Sivabalakrishnan. M, Shanthi. M.” Person Counting System Using EFV Segmentation and Fuzzy Logic,” 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15). [13] Kumar. Rakesh, Parashar. Tapesh, Verma. Gopal.” Background Modeling and Subtraction Based People Counting for Real Time Video Surveillance,” International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-5, November 2012. [14] Mahapatra. A, Mishra. T, Majhi. B.” Background subtraction and human detection in outdoor videos using fuzzy logic,” July 2013 IEEE International Conference on Fuzzy Systems. [15] Alzughaibi. A, Hakami. A, Chaczko. Z.” Review of Human Motion Detection based on Background Subtraction Techniques,” International Journal of Computer Applications (0975 - 8887) Volume 122 - No.13, July 2015. [16] Zhang. L, Liang. Y.” Motion human detection based on background subtraction,” 2010 Second International Workshop on Education Technology and Computer Science. [17] Mahapatra. A, Mishra.Tusar, Sa. K and Majhi. B. “Background Subtraction and Human Detection in Outdoor Videos using Fuzzy Logic,”. 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). [18] Sood. M, Sharma. R, Dipakkumar. C.” Motion Human Detection & Tracking Based on Background Subtraction,” International Journal of Engineering Inventions e-ISSN: 22787461, p-ISSN: 2319-6491 Volume 2, Issue 6 (April 2013) PP: 34-37. [19] Madhavi. B, Rao. M. “A Fast and Reliable Motion Human Detection And Tracking Based On Background Subtraction,” IOSR Journal of Electronics and Communication Engineering (IOSRJECE) ISSN: 2278-2834 Volume 1, Issue 1 (May-June 2012), PP 29-35.
[20] Çelik. Y, Altun. M, Güneş. M.” Color Based Moving Object Tracking with An Active Camera Using Motion Information,” International Conference on Artificial Intelligence and Data Processing (IDAP17), September 16-17, Malatya, Turkey. [21] < https://thingspeak.com/> Accessed:02.04.2018, 23.14.
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