Static and incremental dynamic approaches for multi-objective bitmap join indexes selection in data warehouses

Data warehouses are very large databases and play key role in intelligent decision making in enterprises. The bitmap join indexes selection problem is crucial in the data warehouse physical design and known to be NP-hard. All the existing methods that solve this problem use single objective function and static query workload during the optimization. In the present work, we propose a multi-objective formulation of the problem using I) a static query workload and II) an incremental dynamic query workload. Three best well-known multi-objective evolutionary algorithms, Non-dominated sorting-based genetic algorithm II, S-Metric Selection Evolutionary Multi-Objective Algorithm and Strength Pareto Evolutionary Algorithm 2, are used to solve the multi-objective bitmap join indexes selection problem using both static and incremental dynamic query workloads. A set of experiments are performed to demonstrate the effectiveness of the proposed approaches. The incremental dynamic approach demonstrates a new perspective on bitmap join indexes optimization in a changing environment of an operational data warehouse.