Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
Geoinformatica
The Geometry of Uncertainty in Moving Objects Databases
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Managing uncertainty in moving objects databases
ACM Transactions on Database Systems (TODS)
Mining sequences with temporal annotations
Proceedings of the 2006 ACM symposium on Applied computing
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Mining Frequent Trajectories of Moving Objects for Location Prediction
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
VCCM mining: mining virtual community core members based on gene expression programming
WISI'06 Proceedings of the 2006 international conference on Intelligence and Security Informatics
Hotspot district trajectory prediction
WAIM'10 Proceedings of the 2010 international conference on Web-age information management
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Prediction of uncertain trajectories in moving objects databases has recently become a new paradigm for tracking wireless and mobile devices in an accurate and efficient manner, and is critical in law enforcement applications such as criminal tracking analysis. However, existing approaches for prediction in spatio-temporal databases focus on either mining frequent sequential patterns at a certain geographical position, or constructing kinematical models to approximate real-world routes. The former overlooks the fact that movement patterns of objects are most likely to be local, and constrained in some certain region, while the later fails to take into consideration some important factors, e.g., population distribution, and the structure of traffic networks. To cope with those problems, we propose a general trajectory prediction algorithm called E3TP (an Effective, Efficient, and Easy Trajectory Prediction algorithm), which contains four main phases: (i ) mining "hotspot" regions from moving objects databases; (ii ) discovering frequent sequential routes in hotspot areas; (iii ) computing the speed of a variety of moving objects; and (iv ) predicting the dynamic motion behaviors of objects. Experimental results demonstrate that E3TP is an efficient and effective algorithm for trajectory prediction, and the prediction accuracy is about 30% higher than the naive approach. In addition, it is easy-to-use in real-world scenarios.