Human behavior based particle swarm optimization for materialized view selection in data warehousing environment

Abstract

Because of the Materialized View (MV) space value and repair cost limitation in Data Warehouse (DW) environment, the materialization of all views was practically impossible thus suitable MV selection was one of the smart decisions in building DW to get optimal efficiency, at the same time in the modern world, techniques for enhancing DW quality were appeared continuously such as swarm intelligence. Therefore, this paper presents first framework for speeding up query response time depending on Human Particle Swarm Optimization (HPSO) algorithm for determining the best locations of the views in the DW. The results showed that the proposed method for selecting best MV using HPSO algorithm is better than other algorithms via calculating the ratio of query response time on the base tables of DW and compare it to the response time of the same queries on the MVs. Ratio of implementing the query on the base table takes 14 times more time than the query implementation on the MVs. Where the response time of queries through MVs access equal to 106 milliseconds while by direct access queries equal to 1066 milliseconds. This outlines that the performance of query through MVs access is 1471.698% better than those directly access via DW-logical.