Efficient Distributed RNN Query Processing with Caching

Reverse Nearest Neighbour (RNN) queries play an important role in applications such as internet of vehicles, decision support systems, profile based marketing and so on. Recently, more attention has been paid to the problem of efficient distributed RNN computation in mobile cloud computing environment. A major downside of the existing RNN is its inherent sequential nature and using in-memory algorithm, which limits its applicability to massive data. In this paper, we propose a novel distributed caching based method to efficiently improve the performance of the RNN calculation in a distributed environment. Extensive experiments using both real and synthetic datasets demonstrated that our proposed methods are the state-of-the-art algorithms in scalable RNN queries.