Practical selectivity estimation through adaptive sampling

Abstract
Recently we have proposed an adaptive, random sampling algorithm for general querysize estimation. In earlier work we analyzed the asymptotic efficiency and accuracy of thealgorithm; in this paper we investigate its practicality as applied to selects and joins. First,we extend our previous analysis to provide significantly improved bounds on the amount ofsampling necessary for a given level of accuracy. Next, we provide "sanity bounds" to dealwith queries for which the underlying data is...

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