2016 IEEE 32nd International Conference on Data Engineering (ICDE) (2016)

Helsinki, Finland

May 16, 2016 to May 20, 2016

ISBN: 978-1-5090-2020-1

pp: 966-977

Yu Sun , Department of Computing and Information Systems, University of Melbourne, Australia

Rui Zhang , Department of Computing and Information Systems, University of Melbourne, Australia

Andy Yuan Xue , Department of Computing and Information Systems, University of Melbourne, Australia

Jianzhong Qi , Department of Computing and Information Systems, University of Melbourne, Australia

Xiaoyong Du , Renmin University of China and Key Laboratory of Data Engineering and Knowledge Engineering, MOE, China

ABSTRACT

We study the problem of constructing a reverse nearest neighbor (RNN) heat map by finding the RNN set of every point in a two-dimensional space. Based on the RNN set of a point, we obtain a quantitative influence (i.e., heat) for the point. The heat map provides a global view on the influence distribution in the space, and hence supports exploratory analyses in many applications such as marketing and resource management. To construct such a heat map, we first reduce it to a problem called Region Coloring (RC), which divides the space into disjoint regions within which all the points have the same RNN set. We then propose a novel algorithm named CREST that efficiently solves the RC problem by labeling each region with the heat value of its containing points. In CREST, we propose innovative techniques to avoid processing expensive RNN queries and greatly reduce the number of region labeling operations. We perform detailed analyses on the complexity of CREST and lower bounds of the RC problem, and prove that CREST is asymptotically optimal in the worst case. Extensive experiments with both real and synthetic data sets demonstrate that CREST outperforms alternative algorithms by several orders of magnitude.

INDEX TERMS

Heating, Radio frequency, Indexes

CITATION

Y. Sun, R. Zhang, A. Y. Xue, J. Qi and X. Du, "Reverse nearest neighbor heat maps: A tool for influence exploration,"

*2016 IEEE 32nd International Conference on Data Engineering (ICDE)*, Helsinki, Finland, 2016, pp. 966-977.

doi:10.1109/ICDE.2016.7498305

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