Dynamic histograms for future spatiotemporal range predicates
Recently, in moving object databases that mainly manage the spatiotemporal attributes, approximate query processing for the future location-based queries has deserved enormous attention. Histograms are generally used for selectivity estimation and approximate query answering in database environments. Because histograms static properties may, however make them inappropriate for application areas that treat dynamic properties such as moving object databases, it is necessary to develop several mechanisms that can be well applied to dynamic query processing. In this paper we present a new method to efficiently process the approximate answers for future location-based query predicates on demand by using spatiotemporal histograms. Based on the concepts of entropy and marginal distribution, we build spatiotemporal histograms for the movement parameters, which result in the avoidance of reconstructing histograms. Using spatiotemporal histograms, the approximate future query processing can be achieved efficiently. In addition, we clarify and evaluate our proposed method with several experiments.
histogram moving object approximate query answering spatiotemporal databases