International Telemetering Conference Proceedings / October 30-November 02, 1995 / Riviera Hotel, Las Vegas, Nevada / The Internet, as the online worldwide connection of computers has come to be known, has now grown to the point of emergence as a major tool in many applications. It will soon become, if it has not already become, an indispensable source of information and interaction for scientists and business people alike. The use of the Internet's various protocol's, including mail, newsreader, and file transfer, produces a global interconnectedness that is impossible to achieve in any other fashion. It is also important to realize that the Internet is currently doubling in size every year and will continue to grow at an extremely accelerated rate for at least the next five years. It is therefore important to be aware of the various applications made possible by use of the Internet, and of the potential for telemetry related uses.

Thesis (M.S.)--Washington State University, December 2007. / Includes bibliographical references (p. 66-69).

Thesis (Ph. D.)--University of Hong Kong, 2001. / Includes bibliographical references (leaves 94-98).

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Steven J. Iatrou, Hy Rothstein. Includes bibliographical references (p. 83-84). Also available online.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: HTTP/1.1 -- replicated -- caching -- load balancing -- web content -- distributed. Includes bibliographical references (p. 95-98).

Thesis (Ed. D.)--Lehigh University, 2001. / Includes vita. Includes bibliographical references (leaves 180-198).

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.

The article has been reviewed and accepted for publication in Knowledge Organization 33 (2006) / This is a preprint of a paper to be published in Knowledge Organization. The paper gives a short overview of the history of use of UDC in Internet subject gateways (SGs) with an English interface, from 1993 to 2006. There were in total, nine quality controlled SGs that were functional for shorter or longer periods of time. Their typology and functionality is described. Quality SGs have evolved and the role of classification has changed accordingly from supporting subject organization on the interface and automatic categorization of resources, towards supporting a semantic linking, control and vocabulary mapping between different indexing systems in subject hubs and federated SGs. In this period, many SGs ceased to exist and little information remains available regarding their status. SGs currently using UDC, for some part of their resource organization, do not use a UDC subject hierarchy at the interface and its role in resource indexing has become more difficult to observe. Since 2000, UDC has become more prevalent in East European SGs, portals and hubs, which are outside the scope of this research. This paper is an attempt to provide a record on this particular application of UDC and to offer some consideration of the changes in requirements when it comes to the use of library classification in resource discovery.

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 / Recommender systems are being widely applied in many application settings to suggest products, services, and information items to potential consumers. Collaborative filtering, the most successful recommendation approach, makes recommendations based on past transactions and feedback from consumers sharing similar interests. A major problem limiting the usefulness of collaborative filtering is the sparsity problem, which refers to a situation in which transactional or feedback data is sparse and insufficient to identify similarities in consumer interests. In this article, we propose to deal with this sparsity problem by applying an associative retrieval framework and related spreading activation algorithms to explore transitive associations among consumers through their past transactions and feedback. Such transitive associations are a valuable source of information to help infer consumer interests and can be explored to deal with the sparsity problem. To evaluate the effectiveness of our approach, we have conducted an experimental study using a data set from an online bookstore. We experimented with three spreading activation algorithms including a constrained Leaky Capacitor algorithm, a branch-and-bound serial symbolic search algorithm, and a Hopfield net parallel relaxation search algorithm. These algorithms were compared with several collaborative filtering approaches that do not consider the transitive associations: a simple graph search approach, two variations of the user-based approach, and an item-based approach. Our experimental results indicate that spreading activation-based approaches significantly outperformed the other collaborative filtering methods as measured by recommendation precision, recall, the F-measure, and the rank score.We also observed the over-activation effect of the spreading activation approach, that is, incorporating transitive associations with past transactional data that is not sparse may “dilute” the data used to infer user preferences and lead to degradation in recommendation performance.