A query optimizer requires selectivity estimation of a query to choose the most efficient access plan. An effective method of selectivity estimation for the future locations of moving objects has not yet been proposed. Existing methods for spatial selectivity estimation do not accurately estimate the selectivity of a query to moving objects, because they do not consider the future locations of moving objects, which change continuously as time passes.In this paper, we propose an effective method for spatio-temporal selectivity estimation to solve this problem. We present analytical formulas which accurately calculate the selectivity of a spatio-temporal query as a function of spatio-temporal information. Extensive experimental results show that our proposed method accurately estimates the selectivity over various queries to spatio-temporal data combining real-life spatial data and synthetic temporal data. When Tiger/lines is used as real-life spatial data, the application of an existing method for spatial selectivity estimation to the estimation of the selectivity of a query to moving objects has the average error ratio from 14% to 85%, whereas our method for spatio-temporal selectivity estimation has the average error ratio from 9% to 23%.
['Yongjin Choi', 'Chin-Wan Chung']
Selectivity estimation for spatio-temporal queries to moving objects