Finding Probabilistic Nearest Neighbors for Query Objects with Imprecise Locations

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.