The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Wavelet-based histograms for selectivity estimation
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Self-tuning histograms: building histograms without looking at data
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Multi-dimensional selectivity estimation using compressed histogram information
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
STHoles: a multidimensional workload-aware histogram
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Efficient aggregation over objects with extent
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Indexing Spatio-Temporal Data Warehouses
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Querying about the Past, the Present, and the Future in Spatio-Temporal Databases
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Spatiotemporal Aggregate Computation: A Survey
IEEE Transactions on Knowledge and Data Engineering
Indexing the past, present, and anticipated future positions of moving objects
ACM Transactions on Database Systems (TODS)
A new spatio-temporal prediction approach based on aggregate queries
International Journal of Knowledge and Web Intelligence
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In many applications, such as road traffic supervision and location based mobile service in large cities, moving objects continue to generate large amount of spatio-temporal information in the form of data streams. How to get qualified answers for aggregate queries appears to be a big challenge due to the high dynamic nature of data streams. Previous methods (e.g., AMH[11]) mainly focus on efficient organization of spatio-temporal information and rapid response time, not the quality of the answer. Our main contribution is a novel method to process important aggregate queries (e.g. SUM and AVG) based on a new structure (named AMH*) to summarize spatio-temporal information. The analysis in theory shows that the relative error and (/or) absolute error of answers can be ensured smaller than predefined parameters. A series of extended experiments evaluate the correctness of our approach.