This paper is to evaluate the discovery service WorldCat Discovery (WCD) at Skövde University Library (SUL) through the usability study of the discovery tool WorldCat Discovery (WCD). By reference to the concepts of “Information Portal” and “Next-generation catalogue”, as well as Dillon’s (2001) evaluation model, the overall impression of the discovery service WCD perceived by users at SUL is investigated; the benefits and the problems of the discovery tool WorldCat Discovery are examined and discussed. Data are collected by a two-stage survey among the users of Skövde University Library, which targets on students and researchers at the University of Skövde. The results show that the discovery service WCD is evaluated positively in general and is confirmed to be used in the future by most of the target group members at Skövde University Library. The features of single search interface and basic filter functions are the major benefits, but the access to full-text articles in minor-used languages and metadata quality are the main problems perceived by target group members during performing common search tasks through the WCD interface. By identifying the benefits and problems in relation to the aspects of discover and delivery, this study addresses a cooperative effort between academic libraries, discovery service vendors and content providers to a “seamless” integration of discovery services with academic libraries.

This dissertation was situated in the crossroads of educational data mining (EDM), educational digital libraries (such as the National Science Digital Library; http://nsdl.org), and examination of teacher behaviors while creating online learning resources in an end-user authoring system, the Instructional Architect (IA; http://ia.usu.edu). The knowledge from data/database (KDD) framework for preparing data and finding patterns in large amounts of data served as the process framework in which a latent class analysis (LCA) was applied to IA user data. Details of preprocessing challenges for web usage data are included. A meaningful IA activity framework provided four general areas of user behavior features that assisted in the interpretation of the LCA results: registration and usage, resource collection, project authoring, and project usage. Four clusters were produced on two samples (users with 5–90 logins and those with 10–90 logins) from 22 months of data collection. The analyses produced nearly identical models with both samples. The clusters were named according to their usage behaviors: one-hit wonders who came, did, and left and we are left to wonder where they went; focused functionaries who appeared to produce some content, but in only small numbers and they did not share many of their projects; popular producers who produced small but very public projects that received a lot of visitors; and prolific producers who were very verbose, created many projects, and published a lot to their students with many hits, but they did not publish much for the public. Information about EDM within the context of digital libraries is discussed and implications for the IA, its professional development workshop, and the larger context of educational digital libraries are presented.

This small study investigates the status of accessibility in the context of UK higher education. This is achieved through a case study which evaluates the accessibility status of three UK London-based university digital libraries. The evaluation is performed via the use of a quantitative data collection method, which is examination of conformance to WCAG2.0 standards using online accessibility evaluation tools, and a qualitative method, which is the interviewing and recording of comments by three users with dyslexia evaluating the university digital libraries in real time. The results are then discussed within the framework of the social model of disability and privatised higher education.

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Some classes of Internet users have specific information needs and specialized information-seeking behaviors. For example, educators who are designing a course might create a syllabus, recommend books, create lecture slides, and use tools as lecture aid. All of these resources are available online, but are scattered across a large number of websites. Collecting, linking, and presenting the disparate items related to a given course topic within a digital library will help educators in finding quality educational material. Content quality is important for users. The results of popular search engines typically fail to reflect community input regarding quality of the content. To disseminate information related to the quality of available resources, users need a common place to meet and share their experiences. Online communities can support knowledge-sharing practices (e.g., reviews, ratings). We focus on finding the information needs of educators and helping users to identify potentially useful resources within an educational digital library. This research builds upon the existing 5S digital library (DL) framework. We extend core DL services (e.g., index, search, browse) to include information from latent user groups. We propose a formal definition for the next generation of educational digital libraries. We extend one aspect of this definition to study methods that incorporate collective knowledge within the DL framework. We introduce the concept of deduced social network (DSN) - a network that uses navigation history to deduce connections that are prevalent in an educational digital library. Knowledge gained from the DSN can be used to tailor DL services so as to guide users through the vast information space of educational digital libraries. As our testing ground, we use the AlgoViz and Ensemble portals, both of which have large collections of educational resources and seek to support online communities. We developed two applications, ranking of search results and recommendation, that use the information derived from DSNs. The revised ranking system incorporates social trends into the system, whereas the recommendation system assigns users to a specific group for content recommendation. Both applications show enhanced performance when DSN-derived information is incorporated. / Ph. D.

The dramatic increase in the amount of content available in digital forms gives rise to large-scale digital libraries, targeted to support millions of users and terabytes of data. Retrieving information from a system of this scale in an efficient manner is a challenging task due to the size of the collection as well as the index. This research deals with the design and implementation of an inverted index that supports searching for information in a large-scale digital library, implemented atop a massively parallel storage system. Inverted index partitioning is studied in a simulation environment, aiming at a terabyte of text. As a result, a high performance partitioning scheme is proposed. It combines the best qualities of the term and document partitioning approaches in a new Hybrid Partitioning Scheme. Simulation experiments show that this organization provides good performance over a wide range of conditions. Further, the issues of creation and incremental updates of the index are considered. A disk-based inversion algorithm and an extensible inverted index architecture are described, and experimental results with actual collections are presented. Finally, distributed algorithms to create a parallel inverted index partitioned according to the hybrid scheme are proposed, and performance is measured on a portion of the equipment that normally makes up the 100 node Virginia Tech PetaPlex™ system. NOTE: (02/2007) An updated copy of this ETD was added after there were patron reports of problems with the file. / Ph. D.

Although different authors mean different thing by the term "digital libraries," one common thread is that they include or are built around collections of digital objects. Digital libraries also provide services to large communities, one of which is almost always search. Digital library collections, however, have several characteristic features that make search difficult. They are typically very large. They typically involve many different kinds of objects, including but not limited to books, e-published documents, images, and hypertexts, and often including items as esoteric as subtitled videos, simulations, and entire scientific databases. Even within a category, these objects may have widely different formats and internal structure. Furthermore, they are typically in complex relationships with each other and with such non-library objects as persons, institutions, and events. Relationships are a common feature of traditional libraries in the form of "See / See also" pointers, hierarchical relationships among categories, and relations between bibliographic and non-bibliographic objects such as having an author or being on a subject. Binary relations (typically in the form of directed links) are a common representational tool in computer science for structures from trees and graphs to semantic networks. And in recent years the World-Wide Web has made the construct of linked information objects commonplace for millions. Despite this, relationships have rarely been given "first-class" treatment in digital library collections or software. MARIAN is a digital library system designed and built to store, search over, and retrieve large numbers of diverse objects in a network of relationships. It is designed to run efficiently over large collections of digital library objects. It addresses the problem of object diversity through a system of classes unified by common abilities including searching and presentation. Divergent internal structure is exposed and interpreted using a simple and powerful graphical representation, and varied format through a unified system of presentation. Most importantly, MARIAN collections are designed to specifically include relations in the form of an extensible collection of different sorts of links. This thesis presents MARIAN and argues that it is both effective and efficient. MARIAN is effective in that it provides new and useful functionality to digital library end-users, and in that it makes constructing, modifying, and combining collections easy for library builders and maintainers. MARIAN is efficient since it works from an abstract presentation of search over networked collections to define on the one hand common operations required to implement a broad class of search engines, and on the other performance standards for those operations. Although some operations involve a high minimum cost under the most general assumptions, lower costs can be achieved when additional constraints are present. In particular, it is argued that the statistics of digital library collections can be exploited to obtain significant savings. MARIAN is designed to do exactly that, and in evidence from early versions appears to succeed. In conclusion, MARIAN presents a powerful and flexible platform for retrieval on large, diverse collections of networked information, significantly extending the representation and search capabilities of digital libraries. / Ph. D.

Research on personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users. There it is possible to classify items involved and to personalize based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as digital libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. For this reason, more research on implicit rating data is recommended, because it is easy to obtain, suffers less from terminology issues, is more informative, and contains more user-centered information. In previous reports on my doctoral work, I discussed collecting, storing, processing, and utilizing implicit rating data of digital libraries for analysis and decision support. This dissertation presents a visualization tool, VUDM (Visual User-model Data Mining tool), utilizing implicit rating data, to demonstrate the effectiveness of implicit rating data in characterizing users, user communities, and usage trends of digital libraries. The results of user studies, performed both with typical end-users and with library experts, to test the usefulness of VUDM, support that implicit rating data is useful and can be utilized for digital library analysis software, so that both end users and experts can benefit. / Ph. D.

LibX is a browser extension that provides direct access to library resources. LibX enables users to add additional features to a webpage, such as placing a tutorial video on a digital library homepage. LibX achieves this ability of enhancing web pages through library applications, called LibApps. A LibApp examines a webpage, extracts and processes information of the page, and modifies the web content. It is possible to build an unlimited number of LibApps and enhance web pages in numerous ways. The developers of LibX team cannot build all possible LibApps by themselves. Hence, we decided to create an environment that allows users to create and share LibApps, thereby creating an eco-system of library applications. We developed the LibApp Builder, a cloud-based end-user programming tool that assists users in creating customized library applications with minimal effort. We designed an easy-to-understand meta-design language model with modularized, reusable components. The LibApp language model is designed to hide the complex programming details from the target audiences who are mostly non-technical users, primarily librarians. The LibApp Builder is a web-based editor that allows users to build and test LibApps in an executable environment. A built-in publishing mechanism allows users to group LibApps into packages and publish them in AtomPub format. Any user can directly reuse or adapt published components as required. Two error checking mechanisms have been built into the LibApp Builder viz., type checking and semantic checking to enhance user experience and reduce debugging effort. Additionally, the web interface displays help tooltips to guide users through the process of building a LibApp. We adhered to good software engineering practices such as the agile development model and the model-view-controller design paradigm. The LibApp Builder is built with the ZK AJAX framework and provides a rich interactive user interface. The LibApp Builder is integrated with an optimized full-text, fuzzy search engine and facilitates faceted search by exploiting the BaseX XML database system and XPath/XQuery processor. Users can locate and reuse existing language components through the search interface. To summarize, the LibApp Builder is a community platform for librarians to create, adapt and share LibApps. / Master of Science

This thesis proposes a novel approach for finding tables in text files containing a mixture of unstructured and structured text. Tables may be arbitrarily complex because the data in the tables may themselves be tables and because the grouping of data elements displayed in a table may be very complex. Although investigators have proposed competence models to explain the structure of tables, there are no computationally feasible performance models for detecting and parsing general structures in real data. Our emphasis is placed on the investigation of a new statistical procedure for detecting basic tables in plain text documents. The main task here is defining and testing this theory in the context of the Odessa Digital Library. / Master of Science