Improved Quality View Selection for Analytical Query Performance Enhancement Using Particle Swarm Optimization

Abstract

A data warehouse, which is a central repository of the detailed historical data of an enterprise, is designed primarily for supporting high-volume analytical processing in order to support strategic decision-making. Queries for such decision-making are exploratory, long and intricate in nature and involve the summarization and aggregation of data. Furthermore, the rapidly growing volume of data warehouses makes the response times of queries substantially large. The query response times need to be reduced in order to reduce delays in decision-making. Materializing an appropriate subset of views has been found to be an effective alternative for achieving acceptable response times for analytical queries. This problem, being an NP-Complete problem, can be addressed using swarm intelligence techniques. One such technique, i.e., the similarity interaction operator-based particle swarm optimization (SIPSO), has been used to address this problem. Accordingly, a SIPSO-based view selection algorithm (SIPSOVSA), which selects the Top-[Formula: see text] views from a multidimensional lattice, has been proposed in this paper. Experimental comparison with the most fundamental view selection algorithm shows that the former is able to select relatively better quality Top-[Formula: see text] views for materialization. As a result, the views selected using SIPSOVSA improve the performance of analytical queries that lead to greater efficiency in decision-making.