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Thesis (M.Sc.)--York University, 2007. Graduate Programme in Computer Science. / Typescript. Includes bibliographical references (leaves 120-123). Also available on the Internet. MODE OF ACCESS via web browser by entering the following URL: http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:MR29315

Thesis (Ph. D.)--Worcester Polytechnic Institute. / Keywords: Meta modeling; schema change; frameworks; integration; schema heterogeniety; schema modeling. Includes bibliographical references (p. 381-395).

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

The ability to physically interconnect many distributed, autonomous and heterogeneous software systems on a large scale presents new opportunities for sharing and reuse of existing, and for the creataion of new information and new computational services. However, finding and combining information in many such systems is a challenge even for the most advanced computer users. To address this challenge, mediator systems logically integrate many sources to hide their heterogeneity and distribution and give the users the illusion of a single coherent system. Many new areas, such as scientific collaboration, require cooperation between many autonomous groups willing to share their knowledge. These areas require that the data integration process can be distributed among many autonomous parties, so that large integration solutions can be constructed from smaller ones. For this we propose a decentralized mediation architecture, peer mediator systems (PMS), based on the peer-to-peer (P2P) paradigm. In a PMS, reuse of human effort is achieved through logical composability of the mediators in terms of other mediators and sources by defining mediator views in terms of views in other mediators and sources. Our thesis is that logical composability in a P2P mediation architecture is an important requirement and that composable mediators can be implemented efficiently through query processing techniques. In order to compute answers of queries in a PMS, logical mediator compositions must be translated to query execution plans, where mediators and sources cooperate to compute query answers. The focus of this dissertation is on query processing methods to realize composability in a PMS architecture in an efficient way that scales over the number of mediators. Our contributions consist of an investigation of the interfaces and capabilities for peer mediators, and the design, implementation and experimental study of several query processing techniques that realize composability in an efficient and scalable way.