Particle swarm optimization for bitmap join indexes selection problem in data warehouses

Data warehouses are very large databases usually designed using the star schema. Queries defined on data warehouses are generally complex due to join operations involved. The performance of star schema queries in data warehouses is highly critical and its optimization is hard in general. Several query performance optimization methods exist, such as indexes and table partitioning. In this paper, we propose a new approach based on binary particle swarm optimization for solving the bitmap join index selection problem in data warehouses. This approach selects the optimal set of bitmap join indexes based on a mathematical cost model. Several experiments are performed to demonstrate the effectiveness of the proposed method on the bitmap join index selection problem. Further testing of the method is performed using a database environment specific cost function. The binary particle swarm optimization is found to be more effective than both the genetic algorithm and data mining based approaches.