Thesis (Ph. D.)--Worcester Polytechnic Institute. / Keywords: Replacement strategy; Query rewriting; Query containment; Semantic caching; Query; XML. Includes bibliographical references (p. 210-222).

Thesis (M.S. in Computer Science)--Washington State University. / Includes bibliographical references.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 40-43). Also available in electronic version. Access restricted to campus users.

Thesis (M.S.)--Worcester Polytechnic Institute. / Keywords: stream; runtime optimization; xml; automata; xquery; query algebra. Includes bibliographical references (p. 76-79).

published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

In many emerging applications, such as sensor networks, location-based services, and data integration, the database is inherently uncertain. To handle a large amount of uncertain data, probabilistic databases have been recently proposed, where probabilistic queries are enabled to provide answers with statistical guarantees. In this thesis, we study the important issues of managing the quality of a probabilistic database. We first address the problem of measuring the ambiguity, or quality, of a probabilistic query. This is accomplished by computing the PWS-quality score, a recently proposed measure for quantifying the ambiguity of query answers under the possible world semantics. We study the computation of the PWS-quality for the top-k query. This problem is not trivial, since directly computing the top-k query score is computationally expensive. To tackle this challenge, we propose efficient approximate algorithms for deriving the quality score of a top-k query. We have performed experiments on both synthetic and real data to validate their performance and accuracy. Our second contribution is to study how to use the PWS-quality score to coordinate the process of cleaning uncertain data. Removing ambiguous data from a probabilistic database can often give us a higher-quality query result. However, this operation requires some external knowledge (e.g., an updated value from a sensor source), and is thus not without cost. It is important to choose the correct object to clean, in order to (1) achieve a high quality gain, and (2) incur a low cleaning cost. In this thesis, we examine different cleaning methods for a probabilistic top-k query. We also study an interesting problem where different query users have their own budgets available for cleaning. We demonstrate how an optimal solution, in terms of the lowest cleaning costs, can be achieved, for probabilistic range and maximum queries. An extensive evaluation reveals that these solutions are highly efficient and accurate. / published_or_final_version / Computer Science / Master / Master of Philosophy

Thesis (M.S.)--Brigham Young University. Dept. of Computer Science, 2006. / Includes bibliographical references (p. 81-85).

Thesis (M.S.)--Ohio University, November, 2002. / Title from PDF t.p. Includes bibliographical references (leaves 125-126).

Thesis (M. Phil.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2004. / Includes bibliographical references (leaves 115-125). Also available in electronic version. Access restricted to campus users.