Shortest Average-Distance Query on Heterogeneous Neighboring Objects

Abstract

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 objects closer to each other the heterogeneous neighboring objects (HNOs for short). 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 a novel and important query on the HNOs, namely the shortest averagedistance query (SAvgDQ for short), which can provide useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. Given a query object q and a distance d, the SAvgDQ retrieves a set of HNOs, such that the distances between any two objects in this set are less than or equal to d and its average distance to q is the smallest among all HNOs sets. To efficiently process the SAvgDQ, we develop an algorithm, the SAvgDQ processing algorithm, which operates based on three devised heuristics, the HNOsqualifying heuristic, the HNOs-pruning heuristic, and the SAvgD-pruning heuristic, to 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 algorithm.

Within Skyline Query Processing in Dynamic Road Networks

“…The simulation shows that the WSOU algorithm outperforms the CdSQ algorithm by a factor of 1.5 to three in terms of the CPU time. The large difference in CPU time between the two algorithms comes from: (1) for the CdSQ algorithm, the query re-execution at each update is inevitable, no matter what the number of objects changing attributes is (that is why its cost keeps almost constant); while (2) for the WSOU algorithm, the query re-execution can be avoided by determining which of the objects changing attributes fall outside the distance range d and by only updating the affected query result if necessary, utilizing the OADM, the RDSL and the SOET.…”

“…With the fast advance of positioning techniques in mobile systems and the popularization of portable computers (e.g., laptops, 3G mobile phones and tablet PCs), spatio-temporal databases that aim at efficiently managing a large number of moving objects so as to support various types of location-based queries have attracted much attention in the database community [1][2][3][4]. Many applications, such as geographical information systems, traffic control systems and location-aware advertisements, can benefit from efficient processing of the location-based queries.…”