An Improved Algorithm for Materialized View Selection

The data warehouse is subject oriented, integrated, nonvolatile and time-varying data sets, which is used to support management decision-making. A data warehouse stores materialized views of data from one or more sources, with the purpose of efficiently implementing Decision-support or OLAP queries. One of the most important decisions in designing a data warehouse is the selection of materialized views to be maintained at the warehouse. The materialization of all views is not possible because of the space constraint and maintenance cost constraint. Selecting a suitable set of views that minimize the total cost associated with the materialized views is the key objective of data warehousing. In this paper, first the query cost view selection problem model is proposed. Second, the methods for selecting materialized views are presented. The genetic algorithm is applied to the materialized view selection problem. But with the development of genetic process, the legal solution produced become more and more difficult. Therefore, improved algorithm has been presented in this paper. Finally, in order to test the function and efficiency of our algorithms, experiment simulation is adopted. The experiments show that the given methods can provide nearoptimal solutions in limited time and work well in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.