Multi-objective materialized view selection using MOGA

Materialized views are used as an alternative means for reducing the response time of analytical queries posed against a data warehouse. Since all views cannot be materialized and since optimal view selection is an NP -Hard problem, there is a need to select an appropriate subset of views for materialization that reduce the response times for analytical queries. This problem, referred to as view selection, is a widely studied problem in data warehousing. Several materialized view selection ( MVS ) algorithms exist that address the view selection problem, as a single objective optimization problem where the objective is to minimize the total cost of evaluating all the views ( TVEC ). This cost comprises two costs, i.e. the total cost of evaluation due to materialized views and the total cost of evaluation due to non-materialized views. Minimization of these two costs simultaneously would lead to the minimization of TVEC . In this paper, this bi-objective optimization problem, where the two costs are minimized simultaneously, has been solved using the Multi-Objective Genetic Algorithm ( MOGA ). The proposed MOGA based MVS algorithm selects the Top - K views from a multidimensional lattice with the purpose of achieving an optimal trade-off between the two aforementioned objectives. Materializing these selected Top - K views would reduce the response times for analytical queries and thereby would result in effective and efficient decision making.