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Query Selectivity Estimation via Data Mining

8 years 6 months ago
Query Selectivity Estimation via Data Mining
Estimating the result size of a join is an important query optimization problem as it determines the choice of a good query evaluation strategy. Yet, there are few efficient techniques that solve this problem. We propose a new approach to join selectivity estimation. Our strategy relies on information extracted from stored data in the form of empty joins which represent portions of the two joined tables that produce an empty result. We present experimental results indicating that empty joins are common in real data sets and propose a simple strategy that uses information about empty joins for an improved join selectivity estimation.
Jarek Gryz, Dongming Liang
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where IIS
Authors Jarek Gryz, Dongming Liang
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