Parallelizing Shortest Average-Distance Query Processing

The shortest average-distance query (or SAvgDQ) is a novel type of location-based queries, which can be used to provide useful object information by taking into account the spatial closeness of objects to the query object and the neighboring relationship between objects. Due to a large amount of SAvgDQ that need to be evaluated concurrently, the centralized processing system would suffer a heavy query load, leading eventually to poor performance. As a result, in this paper we focus on distributed processing of multiple SAvgDQ using MapReduce platform. We first design a grid structure to manage information of objects, and then develop an algorithm, namely the MapReduce-based SAvgDQ algorithm (or MRSAvgDQ algorithm), to efficiently process SAvgDQ in a distributed manner.