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Analysis and evaluation of the top-\(k\) most influential location selection query

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Abstract

In this paper, we propose a new type of queries to retrieve the top-k most influential locations from a candidate set \(C\) given sets of customers \(M\) and existing facilities \(F\). The influence models the popularity of a facility. Such queries have wide applications in decision support systems. A naive solution sequentially scans (SS) all data sets, which is expensive, and hence, we investigate two branch-and-bound algorithms for the query, namely Estimate Expanding Pruning (EEP) and Bounding Influence Pruning (BIP). Both algorithms follow the best first traverse. On determining the traversal order, while EEP leverages distance metrics between nodes, BIP relies on half plane pruning which avoids the repetitive estimations in EEP. As our experiments shown, BIP is much faster than SS which outperforms EEP, while the worst-case complexity of EEP and BIP is worse than that of SS. To improve the efficiency, we further propose a Nearest Facility Circle Join (NFCJ) algorithm. NFCJ builds an influence R-tree on the influence relationship between customers and existing facilities and joins the candidate R-tree with the influence R-tree to obtain the results. We compare all algorithms and conclude that NFCJ is the best solution, which outperforms SS, EEP, and BIP by orders of magnitude.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (No. 61272065) and the Natural Science Foundation of Guangdong Province, China (No. S2012010009311), the Fundamental Research Funds for the Central Universities, SCUT(Grant No. 2012ZZ0088), and the Australian Research Council (ARC) Discovery Project DP130104587. Dr. Rui Zhang was supported by the ARC Future Fellowships Project FT120100832. Zeyi Wen was supported by the Commonwealth Scientific and Industrial Research Organisation (CSIRO).

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Authors and Affiliations

  1. School of Software Engineering, South China University of Technology, Guangzhou, China

    Jian Chen

  2. Department of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Jin Huang, Zeyi Wen & Rui Zhang

  3. Department of Computer Science, La Trobe University, Bundoora, Australia

    Zhen He

  4. The Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, Australia

    Kerry Taylor

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  1. Jian Chen
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  2. Jin Huang
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  3. Zeyi Wen
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  4. Zhen He
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  5. Kerry Taylor
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  6. Rui Zhang
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Correspondence to Rui Zhang.

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Chen, J., Huang, J., Wen, Z. et al. Analysis and evaluation of the top-\(k\) most influential location selection query. Knowl Inf Syst 43, 181–217 (2015). https://doi.org/10.1007/s10115-013-0720-0

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  • DOI: https://doi.org/10.1007/s10115-013-0720-0

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