DIM: a distributed air index based on MapReduce for spatial query processing in road networks

• Main Authors: Ran Jin, Gang Chen, Anthony K. H. Tung, Lidan Shou, Beng Chin Ooi, Yuting Gu
• Format: Article
• Language: English
• Published: SpringerOpen 2018-12-01
• Series: EURASIP Journal on Wireless Communications and Networking
• Subjects: Spatial query; Air index; Wireless broadcast; Road networks; kNN
• Online Access: http://link.springer.com/article/10.1186/s13638-018-1287-y

Abstract In this era of mobile application and socialization, location-based services (LBSs) have become unprecedentedly popular and emphasized. By submitting its location to the service provider, the mobile terminal can obtain useful service. As an important technology that can provide location-based services, spatial query processing has become a research hotspot. According to the problems existing in current road network partitioning, first of all, this paper proposes the partition density formula and the optimal road network partitioning method according to the partition density. Based on that, we propose the concepts of partition self-attraction, mutual attraction, and merger factor of partition to effectively merge the partitions with low densities, which can further reduce the number of unnecessary broadcast frames and optimize the index method. Then, we propose the real distributed air index method DIM and kNN spatial query processing algorithm based on the MapReduce platform. According to the DIM index method, the Name Node only stores the prime index, while not storing the data, and each frame consists of defined index and the data in a partition that need to be broadcasted. The mobile user can quickly localize the frame data that they need to obtain according to the main index or the index of current frame, in this way to reduce the tuning time and access latency, which can significantly optimize the query efficiency. Massive experiments have proved the stability and effectiveness of the proposed algorithm.