Thesis (Ph. D.)--University of Texas at Austin, 2000. / Vita. Includes bibliographical references (leaves 193-203). Available also in a digital version from Dissertation Abstracts.

Thesis (M.A.)--Mills College, 2002. / Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Arts in Interdisciplinary Computer Science.

published_or_final_version / abstract / toc / Mathematics / Master / Master of Philosophy

Artificial Intelligence Lab, Department of MIS, University of Arizona / Research in information retrieval (IR) has advanced significantly in the past few decades. Many tasks, such as indexing and text categorization, can be performed automatically with minimal human effort. Machine learning has played an important role in such automation by learning various patterns such as document topics, text structures, and user interests from examples. In recent years, it has become increasingly difficult to search for useful information on the World Wide Web because of its large size and unstructured nature. Useful information and resources are often hidden in the Web. While machine learning has been successfully applied to traditional IR systems, it poses some new challenges to apply these algorithms to the Web due to its large size, link structure, diversity in content and languages, and dynamic nature. On the other hand, such characteristics of the Web also provide interesting patterns and knowledge that do not present in traditional information retrieval systems.

Artificial Intelligence Lab, Department of MIS, University of Arizona / Search engines and data mining are two research areas that have experienced significant progress over the past few years. Overwhelming acceptance of the Internet as a primary medium for content delivery and business transactions has created unique opportunities and challenges for researchers. The richness of the webâ s multimedia content, the reach and timeliness of web-based publication, the proliferation of e-commerce activities and the potential for wireless web delivery have generated many interesting research problems. Technical, system, organizational and social research approaches are all needed to address these research problems. Many interesting webretrieval and mining research topics have emerged recently. These include, but are not limited to, the following: text and data mining on the web, web visualization, web intelligence and agents, web-based decision support and knowledge management, wireless web retrieval and visualization, web-based usability methodology, web-based analysis for eCommerce applications. This special issue consists of nine papers that report research in web retrieval and mining.

This project was created in response to the need for research into web links: including web link mining, and the creation of link metrics. It is aimed at providing the raw data and software for researchers to analyse link structures without having to rely upon commercial search engines, and without having to run their own web crawler. This site will contain all of the following. *Complete databases of link structures of collections of academic web sites. *Files of summary statistics about the link databases. *Software tools for researchers to extract the information that they are particularly interested in. *Descriptions of the methodologies used to crawl the web so that the information provided can be critically evaluated. *Files of information used in the web crawling process.

Univ., Magisterarbeit--München, 2001. / Literaturverz. S. 167 - 172.

Thesis (Ph. D.)--University of Queensland, 2002. / Includes bibliographical references.

Thesis (M.S.)--Youngstown State University, 2009. / Includes bibliographical references (leaf 38). Also available via the World Wide Web in PDF format.

Thesis (M.S.C.I.T.)--Regis University, Denver, Colo., 2005. / Title from PDF title page (viewed on Feb. 17, 2006).