Understanding Cardinality Estimation Using Entropy Maximization
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...
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| Veröffentlicht in: | ACM transactions on database systems 2012-02, Vol.37 (1), p.1-31 |
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| container_title | ACM transactions on database systems |
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| creator | RE, Christopher SUCIU, Dan |
| description | 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. |
| doi_str_mv | 10.1145/2109196.2109202 |
| format | Article |
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| ispartof | ACM transactions on database systems, 2012-02, Vol.37 (1), p.1-31 |
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| language | eng |
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| source | ACM Digital Library Complete |
| subjects | Applied sciences Computer science control theory systems Exact sciences and technology Information systems. Data bases Memory organisation. Data processing Software |
| title | Understanding Cardinality Estimation Using Entropy Maximization |
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