DocumentCode :
1625444
Title :
End-biased Samples for Join Cardinality Estimation
Author :
Estan, Cristian ; Naughton, Jeffrey F.
Author_Institution :
University of Wisconsin-Madison
fYear :
2006
Firstpage :
20
Lastpage :
20
Abstract :
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.
Keywords :
Data engineering; Frequency; Histograms; Monitoring; Multidimensional systems; Probability distribution; Sampling methods; Telecommunication traffic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Engineering, 2006. ICDE '06. Proceedings of the 22nd International Conference on
Print_ISBN :
0-7695-2570-9
Type :
conf
DOI :
10.1109/ICDE.2006.61
Filename :
1617388
Link To Document :
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