Materialized Cube Selection Using Particle Swarm Optimization Algorithm

Storage of pre-computed views in data warehouse can essentially reduce query processing cost for decision support queries. The problem is to choose an optimal set of materialized views. Various frameworks such as lattice, MVPP and AND-OR graphs and algorithms like heuristic based, greedy, stochastic algorithm have been proposed in the literature for materialized view selection. Heuristic and greedy algorithms become slower in high dimensional search space while stochastic algorithms do not guarantee global optimal solution but reach to the optimum most solution in a fast and efficient way. In this paper we have implemented Particle Swarm Optimization (PSO) algorithm, one of the stochastic algorithm, on lattice framework to select an optimal set of views for materialization in data warehouse by minimizing query processing cost. We have compared our results with Genetic algorithm to prove the effectiveness of PSO algorithm over genetic algorithm.