Statistical analysis of sketch estimators
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Sketches for size of join estimation
ACM Transactions on Database Systems (TODS)
The VC-dimension of SQL queries and selectivity estimation through sampling
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Testing cardinality estimation models in SQL server
DBTest '12 Proceedings of the Fifth International Workshop on Testing Database Systems
Journal of Computer and System Sciences
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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 endbiased samples may be a useful addition to the repertoire of techniques used for data summarization.