Location-Based Aggregate Queries for Heterogeneous Neighboring Objects

Currently, most of the processing techniques for the conventional location-based queries focus only on a single type of objects. However, in real-life applications, the user may be interested in obtaining information about different types of objects, in terms of their neighboring relationship. We term the different types of stationary objects closer to each other the heterogeneous neighboring objects (HNOs). Efficient processing of the location-based queries on the HNOs is more complicated than that on a single data source, because the neighboring relationship between the HNOs inevitably affects the query result. In this paper, we present useful and important location-based aggregate queries on the HNOs, which can provide useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. The location-based aggregate queries consist of four queries: the shortest average-distance (SAvgD) query, the shortest minimal-distance (SMinD) query, the shortest maximal-distance (SMaxD) query, and the shortest sum-distance (SSumD) query. To process the location-based aggregate queries, we devise two heuristics, the HNOs-qualifying heuristic and the HNOs-pruning heuristic, to efficiently determine the HNOs sets. According to different query types, we further propose four heuristics, the SAvgD-pruning heuristic, the SMinD-pruning heuristic, the SMaxD-pruning heuristic, and the SSumD-pruning heuristic, to effectively reduce the number of distance computations required for query processing. Comprehensive experiments are conducted to demonstrate the effectiveness of the heuristics and the efficiency of the proposed approaches.