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Nearest neighbor (NN) queries in trajectory databases have receivedsignificant attention in the past, due to their application in spatio-temporaldata analysis. Recent work has considered the realistic case where thetrajectories are uncertain; however, only simple uncertainty models have beenproposed, which do not allow for accurate probabilistic search. In this paper,we fill this gap by addressing probabilistic nearest neighbor queries indatabases with uncertain trajectories modeled by stochastic processes,specifically the Markov chain model. We study three nearest neighbor querysemantics that take as input a query state or trajectory $q$ and a timeinterval. For some queries, we show that no polynomial time solution can befound. For problems that can be solved in PTIME, we present exact queryevaluation algorithms, while for the general case, we propose a sophisticatedsampling approach, which uses Bayesian inference to guarantee that sampledtrajectories conform to the observation data stored in the database. Thissampling approach can be used in Monte-Carlo based approximation solutions. Weinclude an extensive experimental study to support our theoretical results.

Probabilistic Nearest Neighbor Queries on Uncertain Moving Object Trajectories