Selectivity Estimation using Probabilistic Models

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Selectivity Estimation using Probabilistic Models
Estimating the result size of complex queries that involve selection on multiple attributes and the join of several relations is a difficult but fundamental task in database query processing. It arises in cost-based query optimization, query profiling, and approximate query answering. In this paper, we show how probabilistic graphical models can be effectively used for this task as an accurate and compact approximation of the joint frequency distribution of multiple attributes across multiple relations. Probabilistic Relational Models (PRMs) are a recent development that extends graphical statistical models such as Bayesian Networks to relational domains. They represent the statistical dependencies between attributes within a table, and between attributes across foreign-key joins. We provide an efficient algorithm for constructing a PRM from a database, and show how a PRM can be used to compute selectivity estimates for a broad class of queries. One of the major contributions of this ...
Lise Getoor, Benjamin Taskar, Daphne Koller
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2001
Authors Lise Getoor, Benjamin Taskar, Daphne Koller
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