Efficient k-nearest-neighbor search algorthims for historical moving object trajectories
Journal of Computer Science and Technology
Discovery of convoys in trajectory databases
Proceedings of the VLDB Endowment
On-line discovery of flock patterns in spatio-temporal data
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Efficient mutual nearest neighbor query processing for moving object trajectories
Information Sciences: an International Journal
Building ranked mashups of unstructured sources with uncertain information
Proceedings of the VLDB Endowment
Efficient reachability query evaluation in large spatiotemporal contact datasets
Proceedings of the VLDB Endowment
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In applications that produce a large amount of data describing the paths of moving objects, there is a need to ask questions about the interaction of objects over a long recorded history. In this paper, we consider the problem of computing joins over massive moving object histories. The particular join that we study is the "Closest-Point-Of- Approach" join, which asks: Given a massive moving object history, which objects approached within a distance 'd' of one another? We carefully consider several relatively obvious strategies for computing the answer to such a join, and then propose a novel, adaptive join algorithm which naturally alters the way in which it computes the join in response to the characteristics of the underlying data.