Performance Evaluation of Spatio-temporal Selectivity Estimation Techniques

Many novel spatio-temporal applications deal with moving objects. In such environments, a database typically maintains the initial position and the moving function for each object. Instead of updating the database whenever an object position changes (which is not manageable), updates are issued whenever a function parameter (velocity, direction, etc.) changes. For simplicity, we assume that objects move with linear trajectories. Maintaining the moving functions in a database introduces novel problems. For example, the database can answer queries about object positions in the future: ”find all objects that will be in area A, ten minutes from now”. In this paper we present a thorough performance evaluation of techniques for estimating the selectivity of such queries. For many applications, selectivity estimation is more important than exact answers. A traffic monitoring system is a typical example: ”estimate the number of vehicles that will be within 2 miles from the intersection of highways I10 and I405, five minutes from now”. We consider various existing estimators that can be stored in main memory and are updated dynamically. Small size and low update cost are essential requirements for the applicability of such techniques. Furthermore, we propose two new approaches, a technique that uses histograms and a secondary index-based estimator. We run a diverse set of experiments to identify the strengths and weaknesses of every approach, using a wide variety of datasets.