MATERIALIZED VIEWS QUANTUM OPTIMIZED PICKING for INDEPENDENT DATA MARTS QUALITY

Abstract

Particular and timely unified information along with quick and effective query response times is the basic fundamental requirement for the success of any collection of independent data marts (data warehouse) which forms Fact Constellation Schema or Galaxy Schema. Because of the materialized view storage area, the materialization of all views is practically impossible thus suitable materialized views (MVs) picking is one of the intelligent decisions in designing a Fact Constellation Schema to get optimal efficiency. This study presents a framework for picking best-materialized view using Quantum Particle Swarm Optimization (QPSO) algorithm where it is one of the stochastic algorithm in order to achieve the effective combination of good query response time, low query handling cost and low view maintenance cost. The results reveals that the proposed method for picking best-materialized view using QPSO algorithm is better than other techniques via computing the ratio of query response time and compare it to the response time of the same queries on the materialized views. Ratio of implementing the query on the base table takes five times more time than the query implementation on the materialized views. Where the response time of queries through MVs access were found 0.084 seconds while by direct access queries were found 0.422 seconds. This outlines that the performance of query through materialized views access is 402.38% better than those directly access via data warehouse-logical.