University Libraries, with the dawn of Internet era, are compelled to provide relevant information essential to its end users within a short span of time either from its in-house holdings or from resources available in other libraries. This could be made possible only by way of Library Consortia (LC). This paper discusses the planning of LC among the major Campus Libraries of University of Madras. It identifies the need, prerequisites, problems and solutions involved in consortia formation.

Thesis (Ph. D.)--University of North Texas, Dec., 2008. / Title from title page display. Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2007. / "Graduate Program in Communication, Information, and Library Studies." Includes bibliographical references (p. 183-194).

Thesis (Ph. D.)--Stevens Institute of Technology, 1997.

Thesis (Ph. D.)--Princeton University, 1997.

Thesis (Ph.D.)--OGI School of Science & Engineering at OHSU, 2005. / Includes bibliographical references (leaves 112-122).

Achieving interoperability between digital libraries and visualization tools is a difficult problem. To solve this problem, a version of the Open Archives Initiative (OAI) Protocol for Metadata Harvesting called VIDI is proposed. It is a lightweight protocol, which contains only 5 request verbs -- Identify, ListMetadataFormats, ListVisdataFormats, ListTransformers, and RequestResultSet. It is extended from the OAI protocol, which enables its simplicity and wider acceptability. It is flexible, which avoids a rigid architecture in implementation. It is general, so it can apply to all kinds of Visualization Systems and Digital Libraries. But most importantly, it reaches our goal of enabling operability between Visualization Systems and Digital Libraries. The protocol design and implementation details are given. Two prototype systems are implemented to demonstrate the above features. Implementation details are given about ENVISION-ODL and ENVISION-MARIAN. Analysis, evaluation, and conclusions reinforce the discussion of the benefits of VIDI. / Master of Science

Automatic text classification using current approaches is known to perform poorly when documents are noisy or when limited amounts of textual content is available. Yet, many users need access to such documents, which are found in large numbers in digital libraries and in the WWW. If documents are not classified, they are difficult to find when browsing. Further, searching precision suffers when categories cannot be checked, since many documents may be retrieved that would fail to meet category constraints. In this work, we study how different types of evidence from multiple sources can be intelligently fused to improve classification of text documents into predefined categories. We present a classification framework based on an inductive learning method -- Genetic Programming (GP) -- to fuse evidence from multiple sources. We show that good classification is possible with documents which are noisy or which have small amounts of text (e.g., short metadata records) -- if multiple sources of evidence are fused in an intelligent way. The framework is validated through experiments performed on documents in two testbeds. One is the ACM Digital Library (using a subset available in connection with CITIDEL, part of NSF's National Science Digital Library). The other is Web data, in particular that portion associated with the Cadê Web directory. Our studies have shown that improvement can be achieved relative to other machine learning approaches if genetic programming methods are combined with classifiers such as kNN. Extensive analysis was performed to study the results generated through the GP-based fusion approach and to understand key factors that promote good classification. / Ph. D.

We formalize the digital library (DL) integration problem and propose an overall approach based on the 5S (Streams, Structures, Spaces, Scenarios, and Societies) framework. We then apply that framework to integrate domain-specific (archaeological) DLs, illustrating our solutions for key problems in DL integration. An integrated Archaeological DL, ETANA-DL, is used as a case study to justify and evaluate our DL integration approach. We develop a minimum metamodel for archaeological DLs within the 5S theory. We implement the 5SSuite toolkit set to cover the process of union DL generation, including requirements gathering, conceptual modeling, rapid prototyping, and code generation. 5SSuite consists of 5SGraph, 5SGen, and SchemaMapper, which plays an important role during integration. SchemaMapper, a visual mapping tool, maps the schema of diverse DLs into a global schema for a union DL and generates a wrapper for each individual DL. Each wrapper transforms the metadata catalog of its DL to one conforming to the global schema. The converted catalogs are stored in the union catalog, so that the union DL has a global metadata format and union catalog. We also propose a formal approach to DL exploring services for integrated DLs based on 5S, which provides a systematic and functional method to design and implement DL exploring services. Finally, we propose a DL success model to assess integrated DLs from the perspective of DL end users by integrating 5S theory with diverse research on information systems success and adoption models, and information-seeking behavior models. / Ph. D.

The Web, containing a large amount of useful information and resources, is expanding rapidly. Collecting domain-specific documents/information from the Web is one of the most important methods to build digital libraries for the scientific community. Focused Crawlers can selectively retrieve Web documents relevant to a specific domain to build collections for domain-specific search engines or digital libraries. Traditional focused crawlers normally adopting the simple Vector Space Model and local Web search algorithms typically only find relevant Web pages with low precision. Recall also often is low, since they explore a limited sub-graph of the Web that surrounds the starting URL set, and will ignore relevant pages outside this sub-graph. In this work, we investigated how to apply an inductive machine learning algorithm and meta-search technique, to the traditional focused crawling process, to overcome the above mentioned problems and to improve performance. We proposed a novel hybrid focused crawling framework based on Genetic Programming (GP) and meta-search. We showed that our novel hybrid framework can be applied to traditional focused crawlers to accurately find more relevant Web documents for the use of digital libraries and domain-specific search engines. The framework is validated through experiments performed on test documents from the Open Directory Project. Our studies have shown that improvement can be achieved relative to the traditional focused crawler if genetic programming and meta-search methods are introduced into the focused crawling process. / Ph. D.