Query-Condition-Aware Histograms in Selectivity Estimation Method

Abstract

The paper shows an adaptive approach to the query selectivity estimation problem for queries with a range selection condition based on continuous attributes. The selectivity factor estimates a size of data satisfying a query condition. This estimation is calculated at the initial stage of the query processing for choosing the optimal query execution plan. A non-parametric estimator of probability density of attribute values distribution is required for the selectivity calculation. Most of known approaches use equi-width or equi-height histograms as representations of attribute values distributions. The proposed approach uses a new type of histogram based on either an attribute values distribution or a distribution of range bounds of a query selection condition. Applying query-condition-aware histogram lets obtain more accurate selectivity values than using a standard histogram. The approach may be implemented as some extension of query optimizer of DBMS Oracle using ODCI Stats module.