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SIGMOD
2007
ACM

URank: formulation and efficient evaluation of top-k queries in uncertain databases

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URank: formulation and efficient evaluation of top-k queries in uncertain databases
Top-k processing in uncertain databases is semantically and computationally different from traditional top-k processing. The interplay between query scores and data uncertainty makes traditional techniques inapplicable. We introduce URank, a system that processes new probabilistic formulations of top-k queries in uncertain databases. The new formulations are based on marriage of traditional top-k semantics with possible worlds semantics. URank encapsulates a new processing framework that leverages existing query processing capabilities, and implements efficient search strategies that integrate ranking on scores with ranking on probabilities, to obtain meaningful answers for top-k queries. Categories and Subject Descriptors H.2.4 [Database Management]: Systems General Terms Algorithms, Design, Experimentation, Performance Keywords Query Processing, Probabilistic data, Uncertain data, Topk, Ranking
Mohamed A. Soliman, Ihab F. Ilyas, Kevin Chen-Chua
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2007
Where SIGMOD
Authors Mohamed A. Soliman, Ihab F. Ilyas, Kevin Chen-Chuan Chang
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