Abstract: A frequent type of query in a car navigation system is to find the
k nearest neighbors (kNN) of a given
query object (e.g., car) using the actual road network maps. With road networks
(spatial networks), the distances between objects depend on their network
connectivity and it is computationally expensive to compute the distances
(e.g., shortest paths) between objects. In this paper, we propose a novel
approach to efficiently and accurately evaluate kNN queries
in a mobile information system that uses spatial network databases. The
approach uses first order Voronoi diagram and Dijkstra's algorithm. This
approach is based on partitioning a large network to small Voronoi regions, and
then pre-computing distances across the regions. By performing across the
network computation for only the border points of the neighboring regions, we
avoid global pre-computation between every object-pair. Our empirical
experiments with real-world data sets show that our proposed solution
outperforms approaches that are based on on-line distance computation by up to
one order of magnitude. In addition, our approach has better response times
than approaches that are based on pre-computation.