Moving Convolutions and Continuous Probabilistic Nearest-Neighbor Queries for Uncertain Trajectories

This report presents our solution to the problem of processing continuous Nearest Neighbor (NN) queries for moving objects trajectories when the exact position of a given object at a particular time instant is not known, but is bounded by an uncertainty region. As has already been observed in the literature, the answers to continuous NN-queries in spatiotemporal settings are time parameterized in the sense that the objects constituting the answer vary over time. Incorporating uncertainty in the model yields additional attributes that affect the semantics of the answer to this type of queries. In this report, we firstly formalize the impact of uncertainty on the answers to the continuous probabilistic NN-queries (i.e., the semantics of the answer to such queries), and we provide a compact structure for its representation. Then, we propose efficient algorithms for constructing that structure. For practical purposes, it is essential that the results can be incorporated on top of an existing Moving Objects Database, for which we identify syntactic constructs for several qualitative variants of continuous probabilistic NN-queries for uncertain trajectories, and address the problem efficient algorithms for their processing Research supported by the NSF: IIS–0324846, IIS–0713403, OCI–0724806 , ITR IIS-0326284, IIS-0513553, IIS-0812258, IIS0325144/003 Moving Convolutions and Continuous Probabilistic Nearest-Neighbor Queries for Uncertain Trajectories Goce Trajcevski Department of EECS Northwestern University [email protected] Roberto Tamassia ∗ Department of CS Brown University [email protected] Hui Ding Department of EECS Northwestern University [email protected]