Advertisement

A P2P Technique for Continuous k-Nearest-Neighbor Query in Road Networks

  • Fuyu Liu
  • Kien A. Hua
  • Tai T. Do
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4653)

Abstract

Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems.  Many techniques have been proposed to address the server bottleneck including one using distributed servers.  To address both of the scalability factors, P2P computing has been considered.  These schemes enable moving objects to participate as a peer in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates.  Most of these techniques, however, assume an open-space environment.  In this paper, we investigate a P2P computing technique for continuous kNN queries in a network environment.   Since network distance is different from Euclidean distance, techniques designed specifically for an open space cannot be easily adapted for our environment.   We present the details of the proposed technique, and discuss our simulation study.  The performance results indicate that this technique can significantly reduce server workload and wireless communication costs.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cai, Y., Hua, K.A., Cao, G.: Processing Range- Monitoring Queries on Heterogeneous Mobile Objects. In: MDM (2004)Google Scholar
  2. 2.
    Mouratidis, K., Hadjieleftheriou, M., Papadias, D.: Conceptual Partitioning: An Efficient Method for Continuous Nearest Neighbor Monitoring. In: SIGMOD (2005)Google Scholar
  3. 3.
    Gedik, B., Liu, L.: MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K., Ferrari, E. (eds.) EDBT 2004. LNCS, vol. 2992, Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Hu, H., Lee, D.L., Xu, J.: Fast Nearest Neighbor Search on Road Networks. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Boehm, K., Kemper, A., Grust, T., Boehm, C. (eds.) EDBT 2006. LNCS, vol. 3896, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Kolahdouzan, M.R., Shahabi, C.: Voronoi-Based K Nearest Neighbor Search for Spatial Network Databases. In: VLDB, pp. 840–851 (2004)Google Scholar
  6. 6.
    Hu, H., Lee, D.L., Lee, V.C.S.: Distance Indexing on Road Networks. In: VLDB (2006)Google Scholar
  7. 7.
    Xiong, X., Mokbel, M., Aref, W.: SINA: Scalable Incremental Processing of Continuous Queries in Spatio-temporal Databases. In: SIGMOD (2004)Google Scholar
  8. 8.
    Prabhakar, S., Xia, Y., Kalashnikov, D.V., Aref, W.G., Hambrusch, S.E.: Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects. IEEE Trans. on Computers 51(10) (2002)Google Scholar
  9. 9.
    Liu, F., Do, T.T., Hua, K.A.: Dynamic Range Query in Spatial Network Environments. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Shekhar, S., Liu, D.R.: CCAM: A Connectivity-Clustered Access Method for Networks and Network Computations. IEEE TKDE 9(1) (1997)Google Scholar
  11. 11.
    Jensen, C.S., Kolar, J., Pedersen, T.B., Timko, I.: Nearest Neighbor Queries in Road Networks. In: Proc. ACMGIS, pp. 1–8 (2003)Google Scholar
  12. 12.
    Mouratidis, K., Yiu, M.L., Papadias, D., Mamoulis, N.: Continuous Nearest Neighbor Monitoring in Road Networks. In: VLDB, pp. 43–54 (2006)Google Scholar
  13. 13.
    Cho, H., Chung, C.: An Efficient and Scalable Approach to CNN Queries in a Road Network. In VLDB, pp. 865–876 (2005)Google Scholar
  14. 14.
    Wang, H., Zimmermann, R., Ku, W.S.: Distributed Continuous Range Query Processing on Moving Objects. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 655–665. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Hu, H., Xu, J., Lee, D.L.: A Generic Framework for Monitoring Continuous Spatial Queries over Moving Objects. In: SIGMOD (2005)Google Scholar
  16. 16.
    Yu, X., Pu, K.Q., Koudas, N.: Monitoring k-nearest neighbor queries over moving objects. In: IEEE ICDE, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  17. 17.
    Xiong, X., Mokbel, M., Aref, W.: SEA-CNN:Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases. In: IEEE ICDE, IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  18. 18.
    Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query Processing in Spatial Network Databases. In: VLDB (2003)Google Scholar
  19. 19.
    Mouratidis, K., Papadias, D., Bakiras, S., Tao, Y.: A Threshold-Based Algorithm for Continuous Monitoring of k Nearest Neighbors. IEEE TKDE 17(11), 1451–1464 (2005)Google Scholar
  20. 20.
    Wu, W., Guo, W., Tan, K.L.: Distributed Processing of Moving K-Nearest-Neighbor Query on Moving Objects. In: IEEE ICDE, IEEE Computer Society Press, Los Alamitos (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Fuyu Liu
    • 1
  • Kien A. Hua
    • 1
  • Tai T. Do
    • 1
  1. 1.School of EECS, University of Central Florida, Orlando, FLUSA

Personalised recommendations