Relaxed Reverse Nearest Neighbors Queries

  • Arif HidayatEmail author
  • Muhammad Aamir Cheema
  • David Taniar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


Given a set of users U, a set of facilities F, and a query facility q, a reverse nearest neighbors (RNN) query retrieves every user u for which q is its closest facility. Since q is the closest facility of u, the user u is said to be influenced by q. In this paper, we propose a relaxed definition of influence where a user u is said to be influenced by not only its closest facility but also every other facility that is almost as close to u as its closest facility is. Based on this definition of influence, we propose relaxed reverse nearest neighbors (RRNN) queries. Formally, given a value of \(x>1\), an RRNN query q returns every user u for which \(dist(u,q) \le x\times NNDist(u)\) where NNDist(u) denotes the distance between a user u and its nearest facility. Based on effective pruning techniques and several non-trivial observations, we propose an efficient RRNN query processing algorithm. Our extensive experimental study conducted on several real and synthetic data sets demonstrates that our algorithm is several orders of magnitude better than a naïve algorithm as well as a significantly improved version of the naïve algorithm.


Range Query Pruning Technique Minimum Bound Rectangle Pruning Rule Verification Phase 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The research of Muhammad Aamir Cheema is supported by ARC DE130101002 and DP130103405.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Arif Hidayat
    • 1
    Email author
  • Muhammad Aamir Cheema
    • 1
  • David Taniar
    • 1
  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia

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