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End-biased Samples for Join Cardinality Estimation
2006
22nd International Conference on Data Engineering (ICDE'06)
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
doi:10.1109/icde.2006.61
dblp:conf/icde/EstanN06
fatcat:4q7mjlylyjhrvhvxw5kat7kndu