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 ...

• Type: Conference
• Year: 2001
• Where: SIGMOD
• Authors: Lise Getoor, Benjamin Taskar, Daphne Koller
• Keywords: Complex Queries, Database, Potential Queries, SIGMOD 2001, Single-table Multi-attribute Queries