A GPU Accelerated Update Efficient Index for kNN Queries in Road Networks

Abstract

The k nearest neighbor (kNN) query in road networks is a traditional query type in spatial databases. This query has found new applications in the fast-growing location-based services, e.g., finding the k nearest Uber cars of a user for ridesharing. KNN queries in these applications are non-trivial to process due to the frequent location updates of data objects (e.g., movements of the cars). This calls for novel spatial indexes with high efficiency in not only query processing but also handling. To address this need, we propose an index structure that uses a lazy update strategy to reduce the costs of handling without sacrificing query efficiency or answer accuracy. We cache the location updates of data objects and only the corresponding entries in the index when they are queried. We further propose a kNN query algorithm based on this index. This algorithm takes advantage of the strengths of both the CPU and the GPU. It first identifies the queried region and updates the index over this region using the GPU. Then, it uses the GPU to query the index and produce a candidate result set, which is later refined by the CPU to obtain the final query answer. We conduct experiments on real data and compare the proposed algorithm with state-of-the-art kNN algorithms. The experimental results show that the proposed algorithm outperforms the baseline algorithms by orders of magnitude in query time.