In sensor environments and moving robot applications, the position of an object is often known imprecisely because of measurement error and/or movement of the object. In this paper, we present query processing methods for spatial databases in which the position of the query object is imprecisely specified by a probability density function based on a Gaussian distribution. We define the notion of a probabilistic range query by extending the traditional notion of a spatial range query and present three strategies for query processing. Since the qualification probability evaluation of target objects requires numerical integration by a method such as the Monte Carlo method, reduction of the number of candidate objects that should be evaluated has a large impact on query performance. We compare three strategies and their combinations in terms of the experiments and evaluate their effectiveness.

A nearest neighbor query is an important notion in spatial databases and moving object databases. In the emerging application fields of moving object technologies, such as mobile sensors and mobile robotics, the location of an object is often imprecise due to noise and estimation errors. We propose techniques for processing a nearest neighbor query when the location of the query object is specified by an imprecise Gaussian distribution. First, we consider two query processing strategies for pruning candidate objects, which can reduce the number of objects that require numerical integration for computing the qualification probabilities. In addition, we consider a hybrid approach that combines the two strategies. The performance of the proposed methods is evaluated using test data.