World Wide Web

, Volume 17, Issue 6, pp 1261–1293 | Cite as

The min-dist location selection and facility replacement queries

  • Jianzhong QiEmail author
  • Rui Zhang
  • Yanqiu Wang
  • Andy Yuan Xue
  • Ge Yu
  • Lars Kulik


We propose and study a new type of location optimization problem, the min-dist location selection problem: given a set of clients and a set of existing facilities, we select a location from a given set of potential locations for establishing a new facility, so that the average distance between a client and her nearest facility is minimized. The problem has a wide range of applications in urban development simulation, massively multiplayer online games, and decision support systems. We also investigate a variant of the problem, where we consider replacing (instead of adding) a facility while achieving the same optimization goal. We call this variant the min-dist facility replacement problem. We explore two common approaches to location optimization problems and present methods based on those approaches for solving the min-dist location selection problem. However, those methods either need to maintain an extra index or fall short in efficiency. To address their drawbacks, we propose a novel method (named MND), which has very close performance to the fastest method but does not need an extra index. We then utilize the key idea behind MND to approach the min-dist facility replacement problem, which results in two algorithms names MSND and RID. We provide a detailed comparative cost analysis and conduct extensive experiments on the various algorithms. The results show that MND and RID outperform their competitors by orders of magnitude.


Spatial database Geographic information system Location optimization Min-dist metric 


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  1. 1.
    ArcGIS: (2013)
  2. 2.
    Aurenhammer, F.: Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput. Surv. 23, 345–405 (1991)CrossRefGoogle Scholar
  3. 3.
    Brinkhoff, T., Kriegel, H.P., Seeger, B.: Efficient processing of spatial joins using r-trees. In: SIGMOD, pp. 237–246 (1993)Google Scholar
  4. 4.
    Cabello, S., Díaz-Báñez, J.M., Langerman, S., Seara, C., Ventura, I.: Reverse facility location problems. In: CCCG, pp. 68–71 (2005)Google Scholar
  5. 5.
    Delfos, J., Tan, T., Veenendaal, B.: Design of a web-based lbs framework addressing usability, cost, and implementation constraints. World Wide Web 13, 391–418 (2010)CrossRefGoogle Scholar
  6. 6.
    Deng, K., Xu, H., Sadiq, S., Lu, Y., Fung, G.P.C., Shen, H.T.: Processing group nearest group query. In: ICDE, pp. 1144–1147 (2009)Google Scholar
  7. 7.
    Du, Y., Zhang, D., Xia, T.: The optimal-location query. In: SSTD, pp. 163–180 (2005)Google Scholar
  8. 8.
    Gao, Y., Zheng, B., Chen, G., Li, Q.: Optimal-location-selection query processing in spatial databases. IEEE Trans. Knowl. Data Eng. 21, 1162–1177 (2009)CrossRefGoogle Scholar
  9. 9.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: SIGMOD, pp. 47–57 (1984)Google Scholar
  10. 10.
    Hjaltason, G.R., Samet, H.: Ranking in spatial databases. In: SSD, pp. 83–95 (1995)Google Scholar
  11. 11.
    Huang, J., Wen, Z., Qi, J., Zhang, R., Chen, J., He, Z.: Top-k most influential locations selection. In: CIKM (2011)Google Scholar
  12. 12.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. Proc. VLDB Endow. 1(1), 1068–1080 (2008)CrossRefGoogle Scholar
  13. 13.
    Korn, F., Muthukrishnan, S.: Influence sets based on reverse nearest neighbor queries. In: SIGMOD, pp. 201–212 (2000)Google Scholar
  14. 14.
    Mouratidis, K., Papadias, D., Papadimitriou, S.: Medoid queries in large spatial databases. In: SSTD, pp. 55–72 (2005)Google Scholar
  15. 15.
    Nutanong, S., Zhang, R., Tanin, E., Kulik, L.: The v*-diagram: a query-dependent approach to moving knn queries. Proc. VLDB Endow. 1(1), 1095–1106 (2008)CrossRefGoogle Scholar
  16. 16.
    Nutanong, S., Tanin, E., Zhang, R.: Incremental evaluation of visible nearest neighbor queries. IEEE Trans. Knowl. Data Eng. 22(5), 665–681 (2010)CrossRefGoogle Scholar
  17. 17.
    Nutanong, S., Zhang, R., Tanin, E., Kulik, L.: Analysis and evaluation of v*-knn: an efficient algorithm for moving knn queries. VLDB J. 19(3), 307–332 (2010)CrossRefGoogle Scholar
  18. 18.
    Qi, J., Zhang, R., Kulik, L., Lin, D., Xue, Y.: The min-dist location selection query. In: ICDE, pp. 366–377 (2012)Google Scholar
  19. 19.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD, pp. 71–79 (1995)Google Scholar
  20. 20.
    RtreePortal: (2013)
  21. 21.
    Stanoi, I., Riedewald, M., Agrawal, D., Abbadi, A.E.: Discovery of influence sets in frequently updated databases. In: VLDB, pp. 99–108 (2001)Google Scholar
  22. 22.
    Tao, Y., Papadias, D., Lian, X.: Reverse knn search in arbitrary dimensionality. In: VLDB, pp. 744–755 (2004)Google Scholar
  23. 23.
    Wong, R.C.W., Özsu, M.T., Yu, P.S., Fu, A.W.C., Liu, L.: Efficient method for maximizing bichromatic reverse nearest neighbor. Proc. VLDB Endow. 2, 1126–1137 (2009)CrossRefGoogle Scholar
  24. 24.
    Wu, W., Yang, F., Chan, C.Y., Tan, K.L.: Continuous reverse k-nearest-neighbor monitoring. In: MDM (2008)Google Scholar
  25. 25.
    Xia, T., Zhang, D., Kanoulas, E., Du, Y.: On computing top-t most influential spatial sites. In: VLDB, pp. 946–957 (2005)Google Scholar
  26. 26.
    Xiao, X., Yao, B., Li, F.: Optimal location queries in road network databases. In: ICDE, pp. 804–815 (2011)Google Scholar
  27. 27.
    Yang, C., Lin, K.I.: An index structure for efficient reverse nearest neighbor queries. In: ICDE, pp. 485–492 (2001)Google Scholar
  28. 28.
    Yiu, M.L., Papadias, D., Mamoulis, N., Tao, Y.: Reverse nearest neighbors in large graphs. IEEE Trans. Knowl. Data Eng. 18, 540–553 (2006)CrossRefGoogle Scholar
  29. 29.
    Yu, C., Zhang, R., Huang, Y., Xiong, H.: High-dimensional knn joins with incremental updates. Geoinformatica 14, 55–82 (2010)CrossRefGoogle Scholar
  30. 30.
    Zhang, D., Du, Y., Xia, T., Tao, Y.: Progressive computation of the min-dist optimal-location query. In: VLDB, pp. 643–654 (2006)Google Scholar
  31. 31.
    Zhang, R., Lin, D., Kotagiri, R., Bertino, E.: Continuous intersection joins over moving objects. In: ICDE, pp. 863–872 (2008)Google Scholar
  32. 32.
    Zhang, R., Jagadish, H.V., Dai, B.T., Ramamohanarao, K.: Optimized algorithms for predictive range and knn queries on moving objects. Inf. Syst. 35(8), 911–932 (2010)CrossRefGoogle Scholar
  33. 33.
    Zhang, R., Qi, J., Lin, D., Wang, W., Wong, R.C.W.: A highly optimized algorithm for continuous intersection join queries over moving objects. VLDB J. 21, 561–586 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Jianzhong Qi
    • 1
    Email author
  • Rui Zhang
    • 1
  • Yanqiu Wang
    • 2
  • Andy Yuan Xue
    • 1
  • Ge Yu
    • 2
  • Lars Kulik
    • 1
  1. 1.Department of Computing and Information SystemsUniversity of MelbourneMelbourneAustralia
  2. 2.College of Information Science and EngineeringNortheastern UniversityShenyangChina

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