2007 IEEE 23rd International Conference on Data Engineering (2007)
Apr. 15, 2007 to Apr. 20, 2007
Christopher Re , University of Washington, Deptartment of Computer Science, email@example.com
Nilesh Dalvi , University of Washington, Deptartment of Computer Science, firstname.lastname@example.org
Dan Suciu , University of Washington, Deptartment of Computer Science, email@example.com
Modern enterprise applications are forced to deal with unreliable, inconsistent and imprecise information. Probabilistic databases can model such data naturally, but SQL query evaluation on probabilistic databases is difficult: previous approaches have either restricted the SQL queries, or computed approximate probabilities, or did not scale, and it was shown recently that precise query evaluation is theoretically hard. In this paper we describe a novel approach, which computes and ranks efficiently the top-k answers to a SQL query on a probabilistic database. The restriction to top-k answers is natural, since imprecisions in the data often lead to a large number of answers of low quality, and users are interested only in the answers with the highest probabilities. The idea in our algorithm is to run in parallel several Monte-Carlo simulations, one for each candidate answer, and approximate each probability only to the extent needed to compute correctly the top-k answers.
D. Suciu, N. Dalvi and C. Re, "Efficient Top-k Query Evaluation on Probabilistic Data," 2007 IEEE 23rd International Conference on Data Engineering(ICDE), Istanbul, Turkey, 2007, pp. 886-895.