Pruning Search Space of Physical Database Design

Abstract

Very large databases and data warehouses require many optimization structures to speed up their queries. These structures can be classified into two main categories: (1) redundant structures like mono attribute indexes, multi-attribute indexes (bitmap join indexes), materialized views, etc. and (2) no redundant structures, like horizontal partitioning and vertical partitioning. The problem of selecting any of these structures is a very crucial decision for the performance of the data warehouse. In this work, we focus on horizontal partitioning and bitmap join indexes. We first show the similarity between horizontal partitioning and bitmap join indexes. Secondly, we propose a new approach of selecting simultaneously these structures in order to reduce the query processing cost. It consists in using the horizontal partitioning schema obtained by a genetic algorithm to prune the search space of the problem of bitmap join index selection. Thirdly, we propose a greedy algorithm to select bitmap join indexes under a storage bound. Finally, we conduct several experimental studies using an adaptation of APB-1 benchmark in order to validate our proposed algorithms.