Reverse nearest neighbor (RNN) queries in spatial and spatio-temporal databases have received significant attention in the database research community over the last decade. A reverse nearest neighbor (RNN) query finds the objects having a given query object as its nearest neighbor. RNN queries find applications in data mining, marketing analysis, and decision making. Most previous research on RNN queries over trajectory databases assume that the data are certain. In realistic scenarios, however, trajectories are inherently uncertain due to measurement errors or time-discretized sampling. In this paper, we study RNN queries in databases of uncertain trajectories. We propose two types of RNN queries based on a well established model for uncertain spatial temporal data based on stochastic processes, namely the Markov model. To the best of our knowledge our work is the first to consider RNN queries on uncertain trajectory databases in accordance with the possible worlds semantics. We include an extensive experimental evaluation on both real and synthetic data sets to verify our theoretical results.
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