Efficient Multi-dimensional Spatial RkNN Query Processing with MapReduce

Abstract

Reverse k Nearest Neighbor (RkNN) queries are of particular interest in a wide range of data mining applications such as decision support systems, profile based marketing and spatial database etc. With the increasing volume of spatial data, it is difficult to perform RkNN queries efficiently because of the limited computational capability and storage resources. In this paper, we investigate how to perform distributed RkNN queries using MapReduce. Firstly, we investigate the Basic-MRRkNN query method based on the inverted grid index over large scale spatial datasets. Secondly, we propose an optimization method: Lazy-MRRkNN query algorithm that prunes the search space when all data points are discovered. To the best of our knowledge, it is the first time that we propose exact RkNN processing algorithms using MapReduce on multi-dimensional datasets. Extensive experiments using both real and synthetic datasets demonstrated that our proposed methods are efficient and scalable.