PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
DOMINO: databases fOr MovINg Objects tracking
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
On moving object queries: (extended abstract)
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing Animated Objects Using Spatiotemporal Access Methods
IEEE Transactions on Knowledge and Data Engineering
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Analysis of predictive spatio-temporal queries
ACM Transactions on Database Systems (TODS)
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Learning and recognizing the places we go
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
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This paper describes a spatial-temporal indexing method for moving objects with a technique to predict future motion positions of moving objects. To build efficient index structure, we had an experiment to analyze practical moving objects, such as people walking in a hall. As the result, we found that any moving objects can be classified to just three types of motion characteristics; 1) staying, 2) straight moving, and 3) random walking. Indexing systems can predict accurate future positions of each object based on our found characteristics, moreover, the index structure can reduce the cost to update MBRs in spatial-temporal data structure. To show an advantage of our prediction method to previous works, we had an experiment to evaluate performance of each prediction method.