MaxBRkNN Queries for Streaming Geo-Data

  • Hui Luo
  • Farhana M. Choudhury
  • Zhifeng Bao
  • J. Shane Culpepper
  • Bang Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10827)


The problem of maximizing bichromatic reverse k nearest neighbor queries (MaxBR\(k\)NN) has been extensively studied in spatial databases, where given a set of facilities and a set of customers, a MaxBR\(k\)NN query returns a region to establish a new facility p such that p is a \(k\)NN of the maximum number of customers. In the literature, current solutions for MaxBR\(k\)NN queries are predominantly static. However, there are numerous applications for dynamic variations of these queries, including advertisements and resource reallocation based on streaming customer locations via social media check-ins, or GPS location updates from mobile devices. In this paper, we address the problem of continuous MaxBR\(k\)NN queries for streaming objects (customers). As customer data can arrive at a very high rate, we adopt two different models for recency information (sliding windows and micro-batching). We propose an efficient solution where results are incrementally updated by reusing computations from the previous result. We present a safe interval to reduce the number of computations for the new objects, and prune the objects that cannot affect the result. We perform extensive experiments on datasets integrated from four different real-life data sources, and demonstrate the efficiency of our solution by rigorously comparing how different properties of the datasets can affect the performance.



This work was partially supported by ARC DP170102726, DP180102050, and NSFC 61728204, 91646204. Zhifeng Bao is supported by a Google Faculty Award.


  1. 1.
    Cheng, Z., Caverlee, J., Lee, K., Sui, D.Z.: Exploring millions of footprints in location sharing services. In: ICWSM 2011, pp. 81–88 (2011)Google Scholar
  2. 2.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: SIGKDD, pp. 1082–1090 (2011)Google Scholar
  3. 3.
    Ghaemi, P., Shahabi, K., Wilson, J.P., Banaei-Kashani, F.: Continuous maximal reverse nearest neighbor query on spatial networks. In: GIS, pp. 61–70 (2012)Google Scholar
  4. 4.
    Cardinal, J.J., Langerman, S.: Min-max-min geometric facility location problems. In: EWCG, pp. 149–152 (2006)Google Scholar
  5. 5.
    Li, J., Maier, D., Tufte, K., Papadimos, V., Tucker, P.A.: No pane, no gain: efficient evaluation of sliding-window aggregates over data streams. SIGMOD Rec. 34(1), 39–44 (2005)CrossRefGoogle Scholar
  6. 6.
    Lin, H., Chen, F., Gao, Y., Lu, D.: OptRegion: finding optimal region for bichromatic reverse nearest neighbors. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds.) DASFAA 2013. LNCS, vol. 7825, pp. 146–160. Springer, Heidelberg (2013). Scholar
  7. 7.
    Liu, Y., Wong, R.-W., Wang, K., Li, Z., Chen, C., Chen, Z.: A new approach for maximizing bichromatic reverse nearest neighbor search. Knowl. Inf. Syst. 36(1), 23–58 (2013)CrossRefGoogle Scholar
  8. 8.
    Mouratidis, K., Bakiras, S., Papadias, D.: Continuous monitoring of top-k queries over sliding windows. In: SIGMOD, pp. 635–646 (2006)Google Scholar
  9. 9.
    Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group nearest neighbor queries. In: ICDE, pp. 301–312 (2004)Google Scholar
  10. 10.
    Qi, J., Xu, Z., Xue, Y., Wen, Z.: A branch and bound method for min-dist location selection queries. In: ADC, pp. 51–60 (2012)Google Scholar
  11. 11.
    Qi, J., Zhang, R., Kulik, L., Lin, D., Xue, Y.: The min-dist location selection query. In: ICDE, pp. 366–377 (2012)Google Scholar
  12. 12.
    Wong, R.C.-W., Özsu, M.T., Yu, P.S., Fu, A.W.-C., Liu, L.: Efficient method for maximizing bichromatic reverse nearest neighbor. PVLDB 2(1), 1126–1137 (2009)Google Scholar
  13. 13.
    Wong, R.C.-W., Özsu, M.T., Fu, A.W.-C., Yu, P.S., Liu, L., Liu, Y.: Maximizing bichromatic reverse nearest neighbor for lp-norm in two and three-dimensional spaces. PVLDB 20(6), 893–919 (2011)Google Scholar
  14. 14.
    Yan, D., Zhao, Z., Ng, W.: Efficient algorithms for finding optimal meeting point on road networks. PVLDB 4(11), 968–979 (2011)Google Scholar
  15. 15.
    Yan, D., Zhao, Z., Ng, W.: Efficient processing of optimal meeting point queries in Euclidean space and road networks. Knowl. Inf. Syst. 42(2), 319–351 (2015)CrossRefGoogle Scholar
  16. 16.
    Yang, D., Zhang, D., Zheng, V.W., Yu, Z.: Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. Trans. SMC 45(1), 129–142 (2015)Google Scholar
  17. 17.
    Yang, D., Zhang, D., Qu, B.: Participatory cultural mapping based on collective behavior data in location-based social networks. TIST 7(3), 30:1–30:23 (2016)CrossRefGoogle Scholar
  18. 18.
    Zhang, P., Lin, H., Gao, Y., Lu, D.: Aggregate keyword nearest neighbor queries on road networks. GeoInformatica 22, 1–32 (2017)CrossRefGoogle Scholar
  19. 19.
    Zhou, Z., Wu, W., Li, X., Lee, M.L., Hsu, W.: MaxFirst for MaxBRkNN. In: ICDE, pp. 828–839 (2011)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hui Luo
    • 1
  • Farhana M. Choudhury
    • 1
  • Zhifeng Bao
    • 1
  • J. Shane Culpepper
    • 1
  • Bang Zhang
    • 2
  1. 1.School of ScienceRMIT UniversityMelbourneAustralia
  2. 2.CSIROCanberraAustralia

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