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Nearest-neighbor search (NN), which returns the nearest neighbor of a query point in a set of points, is an important and widely studied problem in many fields, and it has wide range of applications. In many of them, such as sensor databases, location-based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest neighbor queries in a probabilistic framework in which the location of each input point is specified as a probability density function. We present efficient algorithms for

- computing all points that are nearest neighbors of a query point with nonzero probability;
- estimating, within a specified additive error, the probability of a point being the nearest neighbor of a query point;
- using it to return the point that maximizes the probability being the nearest neighbor, or all the points with probabilities greater than some threshold to be the \NN.

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Last modified: Sun Mar 24 14:00:48 CDT 2013