Cost and Imprecision in Modeling the Position of Moving Objects
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Prediction and indexing of moving objects with unknown motion patterns
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Mining, indexing, and querying historical spatiotemporal data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Techniques for Efficient Road-Network-Based Tracking of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Continuous Clustering of Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
A Hybrid Prediction Model for Moving Objects
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
WhereNext: a location predictor on trajectory pattern mining
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Traffic density-based discovery of hot routes in road networks
SSTD'07 Proceedings of the 10th international conference on Advances in spatial and temporal databases
TrajPattern: mining sequential patterns from imprecise trajectories of mobile objects
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
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Monitoring the locations of a large number of objects that travel in a certain space is a popular problem for its importance in various application scenarios. It brings us a challenge of how to efficiently handle large volumes of location updates required to guarantee the error bound among an object's current, actual location and its current location in the tracking system. Current solutions predict the future locations based on the recent movements of the moving object. However, it is reliable to predict the position in near future only and the prediction accuracy is poor in the long term. This paper is aimed at the above weakness by introducing the movement pattern in Euclidean space based on the historical trajectories of moving objects. Dominant path pattern is proposed and employed in the moving object tracking system, which can estimate where an object will go next and how to get there. Specifically, dominant path pattern is discovered and indexed by a novel access method of efficient query processing. In addition, the pattern mining techniques with consideration of the accuracy and coverage in dominant path patterns discovering are presented. The experiments demonstrate the superiority of the proposed method comparing to existing methods by up to 73% (91%) less overall location updates on practical Taxi(Truck) dataset.