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 objects 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 will be 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.
Spatiotemporal Database Query Estimation Histogram Grey system theory
LEI BAO MO ZHOU QI-YUAN LI
College of Electronic Engineering, Naval University of Engineering, Wuhan 430033
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1973-1978
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）