Alternative Solutions for Continuous K Nearest Neighbor Queries in Spatial Network Databases

Abstract

Continuous K nearest neighbor queries (C-KNN) are defined as finding the nearest points of interest along an enitre path (e.g., finding the three nearest gas stations to a moving car on any point of a pre-specified path). The result of this type of query is a set of intervals (or split points) and their corresponding KNNs, such that the KNNs of all points within each interval are the same. The current studies on C-KNN focus on vector spaces where the distance between two objects is a function of their spatial attributes (e.g., Euclidean distance metric). These studies are not applicable to spatial network databases (SNDB) where the distance between two objects is a function of the network connectivity (e.g., shortest path between two objects). In this paper, we propose two techniques to address C-KNN queries in SNDB: Intersection Examination (IE) and Upper Bound Algorithm (UBA). With IE, we first find the KNNs of all nodes on a path and then, for those adjacent nodes whose nearest neighbors are different, we find the intermediate split points. Finally, we compute the KNNs of the split points using the KNNs of the surrounding nodes. The intuition behind UBA is that the performance of IE can be improved by determining the adjacent nodes that cannot have any split points in between, and consequently eliminating the computation of KNN queries for those nodes. Our empirical experiments show that the UBA approach outperforms IE, specially when the points of interest are sparsely distributed in the network.