Efficient Evaluation of Shortest Average Distance Query on Heterogeneous Neighboring Objects in Road Networks

Abstract

Recently, the research community has introduced various methods for processing the location-based queries on a single type of objects in road networks. However, in real-life applications user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. The sets of different types of objects closer to each other are termed the heterogeneous neighboring object sets (HNOSs for short). In this paper, we present a novel type of location-based queries, the shortest average distance query (SADQ for short), on the HNOSs in road networks. Given a query object q and a distance d, the SADQ retrieves a HNOS, such that the road distances between any two objects in this set are less than or equal to d and its average road distance to q is the shortest among all HNOSs. As the SADQ provides object information by preserving both the spatial closeness of objects to the query object and the neighboring relationship between objects, it is useful in many fields and application domains. A grid index is first designed to manage information of data objects and road networks, and then the SADQ algorithm is developed, which is combined with the grid index to efficiently process the SADQ. Extensive experiments using real road network datasets demonstrate the efficiency of the proposed SADQ algorithm.