Materialization-bAsed Range and k-nearest neighbor query processing algorithms

Recently, the spatial network databases (SNDB) have been studied for emerging applications such as location-based services including mobile search and car navigation. In practice, objects, like cars and people with mobile phones, can usually move on an underlying network (road, railway, sidewalk, river, etc.), where the network distance is determined by the length of the practical shortest path connecting two objects. In this paper, we propose materialization-based query processing algorithms for typical spatial queries in SNDB, such as range search and k nearest neighbors (k-NN) search. By using a materialization-based technique with the shortest network distances of all the nodes on the network, the proposed query processing algorithms can reduce the computation time of the network distance as well as the number of disk I/Os required for accessing nodes. Thus, the proposed query processing algorithms improve the existing efficient k-NN (INE) and range search (RNE) algorithms proposed by Papadias et al. [1], respectively. It is shown that our range query processing algorithm achieves about up to one of magnitude better performance than RNE and our k-NN query processing algorithm achieves about up to 150% performance improvements over INE.