UPBI An Efficient Index for Continues Probabilistic Range Query of Moving Objects on Road Network

Abstract

With the development of mobile terminal technology, the continuous range query of moving objects on road network has been widely applied in the field of transportation, military and communication. In practical applications, the sampling frequency of positioning equipment could not eliminate uncertainty, resulting in moving objects’ position uncertainty between two adjacent samples. The index existing for continues probabilistic range query are based on the centralized or the traditional cluster distributed environment. In this paper, we construct UPBI index structure for the continuous probability range queries on road network based on Hadoop firstly. Secondly, we design the continuous probability range query parallel algorithm considering moving objects’ position uncertainty on road network. Finally, we simultaneously give space constraint R-restrict and the probability calculation method. The experiment demonstrates that index and query algorithm proposed effectively solve the mass data problem about moving objects, and enhance query efficiency.