Scalable multi-dimensional RNN query processing

Reverse nearest neighbor (RNN) queries are the complimentary problem and particular interest in the past few years, such as location-based services, profile-based marketing, resource allocation, and traffic monitoring system. The one major drawback for the existing RNN is that it has inherent sequential nature and uses in-memory algorithm, which limits its applicability to large-scale spatial data queries. This paper proposes scalable algorithms for RNN queries in a distributed environment. Firstly, we investigate the Basic-scalable reverse nearest neighbor (SRNN) initialization query method based on the inverted grid index. Secondly, two optimization methods Lazy-SRNN and Eager-SRNN are proposed to effectively process scalable multi-dimensional RNN queries. Among them, Lazy-SRNN prunes the search space when all RNN objects are discovered in one pass; Eager-SRNN attempts to prune spatial objects incrementally as soon as they are visited. In addition, the SRNN algorithm is proved to be the first attempt for the exact scalable RNN algorithms in a distributed environment on multi-dimensional data sets. We show in an extensive experimental evaluation on real-world and synthetic data the scalability and the performance of our novel approach. Copyright (c) 2015 John Wiley & Sons, Ltd.