An information theoretic histogram for single dimensional selectivity estimation.

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.