Computer Sciences Dept.

Cristian Estan

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End-biased Samples for Join Cardinality Estimation
Cristian Estan, Jeffrey F. Naughton
ICDE, April 2006

We present a new technique for using samples to estimate join cardinalities. This technique, which we term ``end-biased samples,'' is inspired by recent work in network traffic measurement. It improves on random samples by using coordinated pseudo-random samples and retaining the sampled values in proportion to their frequency. We show that end-biased samples always provide more accurate estimates than random samples with the same sample size. The comparison with histograms is more interesting --- while end-biased histograms are somewhat better than end-biased samples for uncorrelated data sets, end-biased samples dominate by a large margin when the data is correlated. Finally, we compare end-biased samples to the recently proposed ``skimmed sketches'' and show that neither dominates the other, that each has different and compelling strengths and weaknesses. These results suggest that end-biased samples may be a useful addition to the repertoire of techniques used for data summarization.

Paper in PDF and Postscript. Presentation in PowerPoint. The technical report version of this paper also describes an algorithm to numerically compute tighter bounds on the variance of join size estimates.

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