Efficient Evaluation of Probabilistic Advanced Spatial Queries on Existentially Uncertain Data

We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries, nearest neighbors (NNs), spatial skylines, and reverse NNs, and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.