The web has evolved to be the primary carrier of human knowledge during the information age. The ephemeral nature of much web content makes web knowledge preservation vital in preserving human knowledge and memories. Web archives are created to preserve the current web and make it available for future reuse. A growing number of web archive initia- tives are actively engaging in web archiving activities. Web archiving standards like WARC, for formatted storage, have been established to standardize the preservation of web archive data. In addition to its preservation purpose, web archive data is also used as a source for research and for lost information recovery. However, the reuse of web archive data is inherently challenging because of the scale of data size and requirements of big data tools to serve and analyze web archive data efficiently. In this research, we propose to build web archive infrastructure that can support efficient and scalable web archive reuse with big data formats like Parquet, enabling more efficient quantitative data analysis and browsing services. Upon the Hadoop big data processing platform with components like Apache Spark and HBase, we propose to replace the WARC (web archive) data format with a columnar data format Parquet to facilitate more efficient reuse. Such a columnar data format can provide the same features as WARC for long-term preservation. In addition, the columnar data format introduces the potential for better com- putational efficiency and data reuse flexibility. The experiments show that this proposed design can significantly improve quantitative data analysis tasks for common web archive data usage. This design can also serve web archive data for a web browsing service. Unlike the conventional web hosting design for large data, this design primarily works on top of the raw large data in file systems to provide a hybrid environment around web archive reuse. In addition to the standard web archive data, we also integrate Twitter data into our design as part of web archive resources. Twitter is a prominent source of data for researchers in a vari- ety of fields and an integral element of the web's history. However, Twitter data is typically collected through non-standardized tools for different collections. We aggregate the Twitter data from different sources and integrate it into the suggested design for reuse. We are able to greatly increase the processing performance of workloads around social media data by overcoming the data loading bottleneck with a web-archive-like Parquet data format. / Doctor of Philosophy / The web has evolved to be the primary carrier of human knowledge during the information age. The ephemeral nature of much web content makes web knowledge preservation vital in preserving human knowledge and memories. Web archives are created to preserve the current web and make it available for future reuse. In addition to its preservation purpose, web archive data is also used as a source for research and for lost information discovery. However, the reuse of web archive data is inherently challenging because of the scale of data size and requirements of big data tools to serve and analyze web archive data efficiently. In this research, we propose to build a web archive big data processing infrastructure that can support efficient and scalable web archive reuse like quantitative data analysis and browsing services. We adopt industry frameworks and tools to establish a platform that can provide high-performance computation for web archive initiatives and users. We propose to convert the standard web archive data file format to a columnar data format for efficient future reuse. Our experiments show that our proposed design can significantly improve quantitative data analysis tasks for common web archive data usage. Our design can also serve an efficient web browsing service without adopting a sophisticated web hosting architecture. In addition to the standard web archive data, we also integrate Twitter data into our design as a unique web archive resource. Twitter is a prominent source of data for researchers in a variety of fields and an integral element of the web's history. We aggregate the Twitter data from different sources and integrate it into the suggested design for reuse. We are able to greatly increase the processing performance of workloads around social media data by overcoming the data loading bottleneck with a web-archive-like Parquet data format.

This study contributes to the academic literature concerning social tag systems for digital libraries, addressing the identified information gap from the user’s perspective. It defines social tagging tools and tests users’ perceptions about possible practices. Moreover, it evaluates the effect when using social tagging systems in digital libraries, to assess whether such a system enhances the search process, and to identify whether there is any significant relationship between using social tagging systems in digital libraries and user satisfaction. Although developments in the field of social tags have been significant in recent years, there remains an open question regarding their usability, particularly in the context of digital libraries. Therefore, there is a need for further investigation, exploration and evaluation, and so this work contributed to this by exploring the usability of social tagging in digital libraries in terms of accuracy for research, user satisfaction and adoptability. For this study, Saudi students were given the opportunity to use the system in the United Kingdom, and their experiences, and opinions regarding ease of use and adoptability were then analysed to determine if they would assist digital libraries in Saudi Arabia to achieve their educational goals and to ensure user numbers would not decrease. A quantitative approach and a qualitative approach were combined to collect and analyse the data used in this research. The two approaches were conducted in sequential phases. In the first quantitative phase, assessment measures were administrated to Saudi students using library websites while studying in the UK. Data was collected from 175 participants, and statistical analysis was conducted using SPSS. Cross tabulation was also used to describe the numerical data and a chi-square analysis was conducted to determine the relationship between the various study variables. In the follow-up qualitative phase, semi-structured interviews were undertaken with 15 Saudi students, to explore the proposed hypothesis in depth. This data was then thematically analysed. Results concerning the usability of social tagging in digital libraries obtained in western universities cannot be generalised to Saudi Arabian universities, because the context of Saudi Arabia differs culturally and academically (Alsurehi & Al Youbi, 1014). To address this, the study utilised a sample of Saudi Arabian students, who had had the opportunity to experience using social tags while studying abroad, specifically in the United Kingdom. Their experience might potentially be very important and this research could be considered a first attempt to examine the usability of social tags in digital libraries. Since to date few empirical studies have directly addressed the usability issues raised here in Saudi Arabia, this research also offers a contribution in this area. In addition, although this study relates to the Saudi perspective, the findings can also be considered valuable to Arab countries sharing similar cultural and academic traditions.

In today's ever-changing world of technology and information, a growing number of organizations and universities seek to store digital documents in an online, accessible manner. These digital library (DL) repositories are powerful systems that allow institutions to store their digital documents while permitting interaction and collaboration among users in their organizations. Despite the continual work on DL systems that can produce out-of-the-box online repositories, the installation, configuration, and customization processes of these systems are still far from straightforward Motivated by the arduous process of designing digital library instances; installing software packages like DSpace and Greenstone; and configuring, customizing, and populating such systems, we have developed an XML-based model for specifying the nature of DSpace digital libraries. The ability to map out a digital library to be created in a straightforward, XML-based way allows for the integration of such a specification with other DL tools. To make use of DL specifications for DSpace, we create a DL generator that uses these models of digital library systems to create, configure, customize, and populate DLs as specified. We draw heavily on previous work in understanding the nature of digital libraries from the 5S framework for digital libraries. This divides the concerns of digital libraries into a complex, formal representation of the elements that are basic to any minimal digital library system including Streams, Structures, Spaces, Scenarios, and Societies. We reflect on this previous work and provide a fresh application of the 5S framework to practical DL systems. Given our specification and generation process, we draw conclusions towards a more general model that would be suitable to characterize any DL platform with one specification. We present this DSpace DL specification language and generator as an aid to DL designers and others interested in easing the specification of DSpace digital libraries. We believe that our method will not only enable users to create DLs more easily, but also gain a greater understanding about their desired DL structure, software, and digital libraries in general. / Master of Science

This research examined teachers' online behaviors while using a digital library service--the Instructional Architect (IA)--through three consecutive studies. In the first two studies, a statistical model called latent class analysis (LCA) was applied to cluster different groups of IA teachers according to their diverse online behaviors. The third study further examined relationships between teachers' demographic characteristics and their usage patterns. Several user clusters emerged from the LCA results of Study I. These clusters were named isolated islanders, lukewarm teachers, goal-oriented brokerswindow shoppers, key brokers, beneficiaries, classroom practitioners, and dedicated sticky users. In Study II, a cleaning process was applied to the clusters discovered in Study I to further refine distinct user groups. Results revealed three clusters, key brokers, insular classroom practitioners, and ineffective islanders. In Study III, the integration of teacher demographic profiles with clustering results revealed that teaching experience and technology knowledge affected teachers' effectiveness in using the IA. The implication, contributions, and limitation of this research are discussed.

Digital collections have been amassed by institutions and individuals for over two decades. Large collections are becoming increasingly available as resources for research, learning, creativity, and pleasure. However, the value of these collections can remain elusive. Systems and methods are needed to unlock the potential held within collections, to access the knowledge and to make new discoveries with the available information. The aim of this research is to identify and describe a system for interacting with large volumes of digital material that supports both learning and creative development. This is done by investigating the Disc Jockey (DJ) who works with electronic media files. DJs have worked with large digital collections since the birth of file sharing in the 1990s. Their activities necessitate a library system that supports retrieval, creative play, and public presentation of material. The investigation will develop a model of information interaction from their activities. To examine the practice, the research employs an autoethnographic diary study, video interviews, and a practice-led method that combines Grounded Theory with digital interface development. Findings indicate a model of interaction which facilitates learning through the development of a personal collection, and allows creative innovation through key information behaviours of selecting and mixing. The research distinguishes fundamental interface requirements that support the process, and demonstrates transferability of the model to other data representations.

Customized information service is very important for service provider nowadays. Traditional selective dissemination, as widely discussed in library community requires users¡¦ involvement and only serves a limited amount of users. In this thesis, we propose to employ data mining techniques to discover knowledge in circulation databases so as to provide customized service in library new book recommendation. Our research¡¦s data source is from National Sun Yat-Sen University¡¦s library. We follow a standard data mining procedure and report our experience in this thesis. Our research uses patron concept hierarchy and book hierarchy with given support threshold and confidence threshold to derived association rules with patron types being antecedent and book types being subsequent. Four algorithms, namely SBSP, SBMP, LatSBMP, MBMP are proposed to facilitate patron and book hierarchy search. Their complexities are compared analytically.

Abstract Libraries have long been widely recognized as import information-offering institutes. Thousands of new books are acquired per month by our university¡Xa mid-sized university in Taiwan), and patrons may have difficulties identifying the small set of books that really interest them. This gives rise to the problem of finding an effective way to recommend patrons the newly arrived books in a library. In this work, we address this problem in finding generalized association rules between patrons and books. We first discuss how to identify relevant but independent patron attributes in regard of the books they checked out. Then, we propose a set of algorithms for generating large itemsets and evaluate their performance experimentally. In addition, we define interestingness of rules and propose an algorithm for pruning uninteresting rules. Finally, we apply our approach to the circulation data of National SUN Yat-Sen University library and report our experiences.

Literature digital libraries are the source of digitalized literature data, from which Researchers can search for articles that meet their personal interest. However, Users often confused by the large number of articles stored in a digital library and a single query will typically yield a large number of articles, among which only a small subset will indeed interest the user. To provide more effective and efficient information search, many systems are equipped with a recommendation subsystem that recommends articles that users might be interested. In this thesis, we aim to research a number of recommendation techniques for making personalized recommendation. In light of the previous work that used collaborative approach for making recommendation for literature digital libraries, in this thesis, we first propose three content-based recommendation approaches, followed by a set of hybrid approaches that combine both content-based and collaborative methods. These alternatives and approaches were evaluated using the web log of an operational electronic thesis system at NSYSU. It has been found the hybrid approaches yields better quality of articles recommendation.

Breakout session from the Living the Future 7 Conference, April 30-May 3, 2008, University of Arizona Libraries, Tucson, AZ. / Afghanistan workshop attendees seeing their digital access on the computer for the first time. This program will focus on the creation of an Integrated Library System using an Open Source Software for Afghanistan Academic Libraries and on the digitization of Afghanistan's unique resources. Since April 2002, the University of Arizona Libraries' staff have been involved in building capacity for libraries and librarians in Afghanistan. In this program, we will discuss our efforts in working with open source digital libraries’ platforms and customization of the integrated library system interface into both English and the native language of Afghanistan (Persian, Dari), providing and enhancing access to scholarly information resources, and digitizing unique resources, and setting up digitization infrastructure in the country to meet their teaching and research needs. The presentation specifically will reflect on digitization efforts including challenges, problems and barriers with language and lack of technological infrastructure. We will also discuss our virtual training efforts in the absence of training on the ground due to security issues in the country and collaboration efforts with other governmental and non-government entities in Afghanistan and abroad.

In trying to provide access to videogame materials for scholars, collecting organizations must build standards for building and structuring collections, and in turn information professionals must assess the information needs of users. In order to begin the assessment, this paper presents a content analysis of scholarly videogame articles. The results of the analysis will provide the basis for structuring videogame archives, libraries, or databases. Metadata schemas are important to access, and to collecting. That metadata will aid patrons is widely accepted, but too often schemas and vocabularies are based on only experts’ opinions without taking into account patrons’ ideas of what is important. To address this dearth, the content analysis presented in this paper combines historical ideas of metadata standards from expert archivists with an analysis of what themes are important, common, and sought for in the literature of videogame scholars, who are the likely users of videogame collections. / text