Using quantum evolutionary based algorithm to solve materialized view selection problem

A Data warehouse is a structure that stores big amount of data. This data is exploited in the best possible ways in order to improve the efficiency of decision-making. The huge volume of data makes answering queries complex and time-consuming. Therefore, materialized views are used in order to reduce the query processing time. Since materializing all views is not possible, due to space and maintenance constraints, materialized view selection became one of the crucial decisions in designing a data warehouse for optimal efficiency. In this paper, the authors propose a Quantum Evolutionary based algorithm named QEAM to solve the materialized view selection (MVS) problem with storage space constraint. The experimental results show the efficiency of the proposed algorithm compared to well-known algorithms used to solve MVS problem with storage space constraint.