Solving View Selection Problem to Query Plan Using Meta Heuristic Methods

Selecting views to materialize is one of the most important decisions in designing a data warehouse. Decision support systems usually use complex queries on large databases. Because responding time should be small, therefore query plan is very important. In this paper, we submit four advances in view materialization. First, a more robust optimization function, Minimum View Selection Problem, and data cube lattices as well as multi-view processing plan (MVPP) are formalized. The decreased View Selection Problem allows for multiple querying nodes, partial and full materialization, and data constriction. The contribution of the research is to select an appropriate set of views that minimize total query response time and the cost of selected views. The traditional method ineffective, using Polynomial Greedy Algorithm (PGA) and genetic algorithm (GA), views of the Data Warehouse can be optimized .: