In order to facilitate query processing, the information contained in data warehouses is typically stored as a set of materialized views. Deciding which views to materialize presents a considerable challenge. The task is to select from a very large search space a set of views that minimizes view maintence and query processing costs. Heuristic methods have been employed to find near optimal solutions and recent genetic algorithms have significantly improved the quality of the obtained solutions. In this paper we introduce a new approach for materialized view selection that is based on Simulated Annealing in conjunction with the use of a Multiple View Processing Plan (MVPP). Our experiments show that our new method provides a further significant improvement in the quality of the obtained set of materialized views, leading to a further significant improvement in query processing time and view maintence costs for data warehousing systems.
The IASTED International Conference on Databases and Applications - DBA 2006 (2006)
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