Abstract
A frequent type of query in a car navigation system is to find theknearest 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 evaluatekNN 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.
Highlights
Over the last decade, due the rapid developments in information technology (IT), communication technologies, a new breed of information systems have appeared such as mobile information systems
With spatial network databases (SNDB), objects are restricted to move on pre-defined paths that are specified by an underlying network
In this paper we presented a novel approach for k nearest neighbor queries in spatial network databases
Summary
Due the rapid developments in information technology (IT), communication technologies, a new breed of information systems have appeared such as mobile information systems. Starting from the query point q we perform network expansion two different scales simultaneously to: 1) compute the distance from q to its first nearest neighbor (its Voronoi cell center point), and 2) explore the objects. A network expansion similar to INE performed inside the Voronoi cell that contains q (V C(q)) starting from q To this end, we utilize the actual network links (e.g., roads) and nodes (e.g., restaurants, hospitals) to compute the distance from q (e.g., vehicle) to its first nearest neighbor (the generator point of V C(q)) and the border points of V C(q).
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