A “semi-lazy” approach to probabilistic path prediction in dynamic environments
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
TODMIS: mining communities from trajectories
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Spatiotemporal periodical pattern mining in traffic data
Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing
A framework of traveling companion discovery on trajectory data streams
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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The advance of object tracking technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data stream. In this study, we investigate the problem of discovering object groups that travel together (i.e., traveling companions) from trajectory stream. Such technique has broad applications in the areas of scientific study, transportation management and military surveillance. To discover traveling companions, the monitoring system should cluster the objects of each snapshot and intersect the clustering results to retrieve moving-together objects. Since both clustering and intersection steps involve high computational overhead, the key issue of companion discovery is to improve the algorithm's efficiency. We propose the models of closed companion candidates and smart intersection to accelerate data processing. A new data structure termed traveling buddy is designed to facilitate scalable and flexible companion discovery on trajectory stream. The traveling buddies are micro-groups of objects that are tightly bound together. By only storing the object relationships rather than their spatial coordinates, the buddies can be dynamically maintained along trajectory stream with low cost. Based on traveling buddies, the system can discover companions without accessing the object details. The proposed methods are evaluated with extensive experiments on both real and synthetic datasets. The buddy-based method is an order of magnitude faster than existing methods. It also outperforms other competitors with higher precision and recall in companion discovery.