Materialized View Selection Using Genetic Algorithm

A data warehouse stores historical information, integrated from several large heterogeneous data sources spread across the globe, for the purpose of supporting decision making. The queries for decision making are usually analytical and complex in nature and their response time is high when processed against a large data warehouse. This query response time can be reduced by materializing views over a data warehouse. Since all views cannot be materialized, due to space constraints, and optimal selection of subsets of views is an NP-complete problem, there is a need for selecting appropriate subsets of views for materialization. An approach for selecting such subsets of views using Genetic Algorithm is proposed in this paper. This approach computes the top-T views from a multidimensional lattice by exploring and exploiting the search space containing all possible views. Further, this approach, in comparison to the greedy algorithm, is able to comparatively lower the total cost of evaluating all the views.

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