Voronoi-Based K Nearest Neighbor Search for Spatial Network Databases

A frequent type of query in spatial networks (e.g., road networks) is to find the K nearest neighbors (KNN) of a given query object. With these 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 spatial network databases using first order Voronoi diagram. This approach is based on partitioning a large network to small Voronoi regions, and then pre-computing distances both within and across the regions. By localizing the precomputation within the regions, we save on both storage and computation and by performing across-the-network computation for only the border points of the neighboring regions, we avoid global pre-computation between every node-pair. Our empirical experiments with several real-world data sets show that our proposed solution outperforms approache...

• Type: Conference
• Year: 2004
• Where: VLDB
• Authors: Mohammad R. Kolahdouzan, Cyrus Shahabi
• Keywords: Database, Small Voronoi Regions, Spatial Network, VLDB 2004, first Order Voronoi