Improving Range Query Result Size Estimation Based on a New Optimal Histogram

Abstract

Many commercial relational Data Base Management Systems (DBMSs) maintain histograms to approximate the distribution of values in the relation attributes and based on them estimate query result sizes. A histogram approximates the distribution by grouping data into buckets. The estimation-errors resulting from the loss of information during the grouping process affect the accuracy of the decision, made by query optimizers, about choosing the most economical evaluation plan for a query. In front of this challenging problem, many histogram-based estimation techniques including the equi-depth, the v-optimal, the max-diff and the compressed histograms have well contributed to approximate the cost of a query evaluation plan. But, most of the times the obtained estimates have much error. Motivated by the fact that inaccurate estimations can lead to wrong decisions, we propose in this paper an efficient algorithm, called Compressed-V2, for accurate histogram constructions. Both theoretical and effective experiments are done using benchmark data set showing the promising results obtained using the proposed algorithm. We think that this algorithm will significantly contribute for helping to solve the problem of Multi-Query Optimization (MQO) resulting from queries interactions especially in Relational Data Warehouses (RDW) which represent the ideal environment in which complex OLAP queries interact with each other.