Understanding cardinality estimation using entropy maximization

Author:

Ré Christopher1,Suciu D.2

Affiliation:

1. University of Wisconsin--Madison

2. University of Washington, Seattle

Abstract

Cardinality estimation is the problem of estimating the number of tuples returned by a query; it is a fundamentally important task in data management, used in query optimization, progress estimation, and resource provisioning. We study cardinality estimation in a principled framework: given a set of statistical assertions about the number of tuples returned by a fixed set of queries, predict the number of tuples returned by a new query. We model this problem using the probability space, over possible worlds, that satisfies all provided statistical assertions and maximizes entropy. We call this the Entropy Maximization model for statistics (MaxEnt). In this article we develop the mathematical techniques needed to use the MaxEnt model for predicting the cardinality of conjunctive queries.

Funder

Air Force Research Laboratory

Office of Naval Research

Division of Information and Intelligent Systems

Publisher

Association for Computing Machinery (ACM)

Subject

Information Systems

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