Processing Location-Based Aggregate Queries in Road Networks

In recent years, 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 (or HNO sets for short). To provide users with object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects, we present useful and important location-based aggregate queries on finding the HNO sets in road net-works. The location-based aggregate queries are the shortest average distance query (SAvgDQ), the shortest minimal distance query (SMinDQ), the shortest maximal distance query (SMaxDQ), and the shortest sum distance query (SSumDQ). We first utilize a grid index to manage information of data objects and road networks, and then propose the SAvgDQ, SMinDQ, SMaxDQ, and SSumDQ processing algorithms, which are combined with the grid index to efficiently process the four types of location-based aggregate queries, respectively. A comprehensive set of experiments is conducted to demonstrate the efficiency of the proposed processing algorithms using real road network datasets.