Distance indexing on road networks

The processing of kNN and continuous kNN queries on spatial network databases (SNDB) has been intensively studied recently. However, there is a lack of systematic study on the computation of network distances, which is the most fundamental difference between a road network and a Euclidean space. Since the online Dijkstra's algorithm has been shown to be efficient only for short distances, we propose an efficient index, called distance signature, for distance computation and query processing over long distances. Distance signature discretizes the distances between objects and network nodes into categories and then encodes these categories. To minimize the storage and search costs, we present the optimal category partition, and the encoding and compression algorithms for the signatures, based on a simplified network topology. By mathematical analysis and experimental study, we showed that the signature index is efficient and robust for various data distributions, query workloads, parameter settings and network updates.