An information theoretic histogram for single dimensional selectivity estimation

Authors:
Chris Giannella;Bassem Sayrafi
Affiliations:
Univ. of Maryland Baltimore County, Baltimore MD;Indiana Univ., Bloomington IN
Venue:
Proceedings of the 2005 ACM symposium on Applied computing
Year:
2005

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Abstract

We study the problem of one dimensional selectivity estimation in relational databases. We introduce a new type of histogram based on information theory. We compare our histogram against a large number of other techniques and on a wide array of datasets. We observe our histograms to have the overall best accuracy on the real datasets. We also observe that the accuracy ranking of all methods varies significantly across datasets. As such, we observe results not consistent with several conclusions drawn in past literature. Thus, we believe a gap exists in the past accuracy characterization.