A Histogram-Based Grey Estimator for Spatiotemporal Selective Queries

Spatiotemporal databases need to process vast amounts of data. In such cases, generating summarized information from the data set is more useful than individually analyzing every entry and the selectivity estimation is more important than exact answer. In this paper, we introduce a histogram-based grey estimator for spatiotemporal selectivity estimation, the basic idea is that although the individual object´s movements has much randomness, the overall data distribution varies gradually with time, due to the continuity of movement. Using prediction models on the history and present query results, it is more accurate to get query estimate than using existing linear extrapolating spatiotemporal selectivity estimation techniques. To enhance the estimation performance, grey prediction model GM(1,1) is used, which can reduce the randomness inside the history query results sequence and generates its holistic measure. Comparisons to traditional approaches show that as randomness of history query results increasing, the near future prediction results of spatiotemporal window queries remain accurate and stable