  Nearest-neighbor search, 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 distribution function. We present efficient algorithms for
  - computing all points that are nearest neighbors of a query point with
nonzero probability; and
  - estimating the probability of a point being the nearest neighbor of a query
point, either exactly or within a specified additive error.
