Despite the importance of spatial networks in real-life applications, most of the spatial database literature focuses on Euclidean spaces. In this paper we propose an architecture that integrates network and Euclidean information, capturing pragmatic constraints. Based on this architecture, we develop a Euclidean restriction and a network expansion framework that take advantage of location and connectivity to efficiently prune the search space. These frameworks are successfully applied to the most popular spatial queries, namely nearest neighbors, range search, closest pairs and edistance joins, in the context of spatial network databases.
Papadias et al. in REF propose a solution for nearest neighbor queries in network databases by introducing an architecture that integrates network and Euclidean information and captures pragmatic constraints.
Query Processing in Spatial Network Databases
VLDB
Computer Science