A Unified Algorithm for Continuous Monitoring of Spatial Queries

  • Mahady Hasan
  • Muhammad Aamir Cheema
  • Xuemin Lin
  • Wenjie Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6588)


Continuous monitoring of spatial queries has gained significant research attention in the past few years. Although numerous algorithms have been proposed to solve specific queries, there does not exist a unified algorithm that solves a broad class of spatial queries. In this paper, we first define a versatile top-k query and show that various important spatial queries can be modeled to a versatile top-k query by defining a suitable scoring function. Then, we propose an efficient algorithm to continuously monitor the versatile top-k queries. To show the effectiveness of our proposed approach, we model various inherently different spatial queries to the versatile top-k query and conduct experiments to show the efficiency of our unified algorithm. The extensive experimental results demonstrate that our unified algorithm is several times faster than the existing best known algorithms for monitoring constrained k nearest neighbors queries, furthest k neighbors queries and aggregate k nearest neighbors queries.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bae, S.W., Korman, M., Tokuyama, T.: All farthest neighbors in the presence of highways and obstacles. In: Das, S., Uehara, R. (eds.) WALCOM 2009. LNCS, vol. 5431, pp. 71–82. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)CrossRefMATHGoogle Scholar
  3. 3.
    Cheema, M.A., Brankovic, L., Lin, X., Zhang, W., Wang, W.: Multi-guarded safe zone: An effective technique to monitor moving circular range queries. In: ICDE, pp. 189–200 (2010)Google Scholar
  4. 4.
    Cheema, M.A., Lin, X., Zhang, Y., Wang, W., Zhang, W.: Lazy updates: An efficient technique to continuously monitoring reverse knn. VLDB 2(1), 1138–1149 (2009)Google Scholar
  5. 5.
    Cheema, M.A., Yuan, Y., Lin, X.: CircularTrip: An effective algorithm for continuous kNN queries. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 863–869. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Chen, Z., Ness, J.W.V.: Characterizations of nearest and farthest neighbor algorithms by clustering admissibility conditions. Pattern Recognition 31(10), 1573–1578 (1998)CrossRefGoogle Scholar
  7. 7.
    Ferhatosmanoglu, H., Stanoi, I., Agrawal, D.P., Abbadi, A.E.: Constrained nearest neighbor queries. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 257–278. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Gedik, B., Liu, L.: Mobieyes: Distributed processing of continuously moving queries on moving objects in a mobile system. In: EDBT, pp. 67–87 (2004)Google Scholar
  9. 9.
    Hasan, M., Cheema, M.A., Qu, W., Lin, X.: Efficient algorithms to monitor continuous constrained k nearest neighbor queries. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5981, pp. 233–249. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Henrich, A.: A distance scan algorithm for spatial access structures. In: ACM-GIS, pp. 136–143 (1994)Google Scholar
  11. 11.
    Hjaltason, G.R., Samet, H.: Ranking in spatial databases. In: Egenhofer, M.J., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 83–95. Springer, Heidelberg (1995)CrossRefGoogle Scholar
  12. 12.
    Lazaridis, I., Porkaew, K., Mehrotra, S.: Dynamic queries over mobile objects. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Hwang, J., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 269–286. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Luo, Y., Chen, H., Furuse, K., Ohbo, N.: Efficient methods in finding aggregate nearest neighbor by projection-based filtering. In: Gervasi, O., Gavrilova, M.L. (eds.) ICCSA 2007, Part III. LNCS, vol. 4707, pp. 821–833. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Mouratidis, K., Hadjieleftheriou, M., Papadias, D.: Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. In: SIGMOD, pp. 634–645 (2005)Google Scholar
  15. 15.
    Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Trans. Database Syst. 30(2), 529–576 (2005)CrossRefGoogle Scholar
  16. 16.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD, pp. 71–79 (1995)Google Scholar
  17. 17.
    Suri, S.: Computing geodesic furthest neighbors in simple polygons. J. Comput. Syst. Sci. 39(2), 220–235 (1989)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Wu, K.L., Chen, S.K., Yu, P.S.: Incremental processing of continual range queries over moving objects. IEEE Trans. Knowl. Data Eng. 18(11), 1560–1575 (2006)CrossRefGoogle Scholar
  19. 19.
    Wu, W., Tan, K.L.: isee: Efficient continuous k-nearest-neighbor monitoring over moving objects. In: SSDBM, p. 36 (2007)Google Scholar
  20. 20.
    Xiong, X., Mokbel, M.F., Aref, W.G.: Sea-cnn: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In: ICDE, pp. 643–654 (2005)Google Scholar
  21. 21.
    Yiu, M.L., Mamoulis, N., Papadias, D.: Aggregate nearest neighbor queries in road networks. IEEE Trans. Knowl. Data Eng. 17(6), 820–833 (2005)CrossRefGoogle Scholar
  22. 22.
    Yu, X., Pu, K.Q., Koudas, N.: Monitoring k-nearest neighbor queries over moving objects. In: ICDE, pp. 631–642 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mahady Hasan
    • 1
  • Muhammad Aamir Cheema
    • 1
  • Xuemin Lin
    • 1
  • Wenjie Zhang
    • 1
  1. 1.The University of New South WalesAustralia

Personalised recommendations