Equi-depth multidimensional histograms
SIGMOD '88 Proceedings of the 1988 ACM SIGMOD international conference on Management of data
Proceedings of the sixteenth international conference on Very large databases
Approximation algorithms for geometric median problems
Information Processing Letters
Histogram-based estimation techniques in database systems
Histogram-based estimation techniques in database systems
A constant-factor approximation algorithm for the k-median problem (extended abstract)
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Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Indexing moving points (extended abstract)
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A data model and data structures for moving objects databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Indexing the positions of continuously moving objects
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Local search heuristic for k-median and facility location problems
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Selectivity estimation for spatio-temporal queries to moving objects
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An Efficient Indexing Scheme for Multi-dimensional Moving Objects
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Histogram-Based Approximation of Set-Valued Query-Answers
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Universality of Serial Histograms
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Performance evaluation of spatio-temporal selectivity estimation techniques
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Spatial Selectivity Estimation Using Cumulative Density Wavelet Histogram
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
Clustering moving objects in spatial networks
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ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Many spatio-temporal applications involve managing and querying moving objects. In such an environment, predictive spatio-temporal queries become an important query class to be processed to capture the nature of moving objects. In this paper, we investigated the problem of selectivity estimation for predictive spatio-temporal queries. We propose a novel histogram technique based on a clustering paradigm. To avoid expensive computation costs, we developed linear time heuristics to construct such a histogram. Our performance study indicated that the new techniques improve the accuracy of the existing techniques by one order of magnitude.