A new approach to building histogram for selectivity estimation in query processing optimization

Recently, histograms have been considered as an effective way to produce quick approximate answers to decision support queries. They are also taken as a basic tool for data visualization and analysis. In this paper, we propose a new approach to constructing histograms for selectivity estimation in query processing optimization. Our approach uses a new criterion, i.e., aggregate error minimization, to direct the construction of the target histogram. We develop the algorithm of aggregate error minimization based histogram construction, and demonstrate the effectiveness and efficiency of the proposed approach by experiments over both real-world and synthetic datasets.