Dynamic modeling of trajectory patterns using data mining and reverse engineering
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
Mining frequent trajectory patterns in spatial-temporal databases
Information Sciences: an International Journal
A filter-and-refine approach to mine spatiotemporal co-occurrences
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Given a collection of trajectories of moving objects with different types (e.g., pumas, deers, vultures, etc.), we introduce the problem of discovering collocation episodes in them (e.g., if a puma is moving near a deer, then a vulture is also going to move close to the same deer with high probability within the next 3 minutes). Collocation episodes catch the inter-movement regularities among different types of objects. We formally define the problem of mining collocation episodes and propose two scaleable algorithms for its efficient solution. We empirically evaluate the performance of the proposed methods using synthetically generated data that emulate real-world object movements.