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
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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We propose a spatial-temporal indexing method for moving objects based on a prediction technique using motion patterns extracted from practical data, such as trajectories of pedestrians. To build an efficient index structure, we conducted an experiment to analyze practical moving objects, such as people walking in a hall. As a result, we found that any moving objects can be classified into just three types of motion characteristics: 1) staying, 2) straight-moving, 3) random walking. Indexing systems can predict highly accurate future positions of each object based on our found characteristics; moreover, the indexing system can build efficient MBRs in the spatial-temporal data structure. To show the advantage of our prediction method over previous works, we conducted an experiment to evaluate the performance of each prediction method.