Multi-dimensional selectivity estimation method using compressed histogram information

Abstract

Disclosed is a multi-dimensional selectivity estimation method using compressed histogram information which the database query optimizer in a database management system uses to find the most efficient execution plan among all possible plans. The method includes the several steps to generate a large number of small-sized multi-dimensional histogram buckets, sampling DCT coefficients which have high values with high probability, compressing information from the multi-dimensional histogram buckets using a multi-dimensional discrete cosine transform(DCT) and storing compressed information, and estimating the query selectivity by using compressed and stored histogram information as the statistics.