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Continuous K-Nearest Neighbor Query over Moving Objects in Road Networks

  • Yuan-Ko Huang
  • Zhi-Wei Chen
  • Chiang Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5446)

Abstract

Continuous K-Nearest Neighbor (CKNN) query is an important type of spatio-temporal queries. A CKNN query is to find among all moving objects the K-nearest neighbors (KNNs) of a moving query object at each timestamp. In this paper, we focus on processing such a CKNN query in road networks, where the criterion for determining the KNNs is the shortest network distance between objects. We first highlight the limitations of the existing approaches, and then propose a cost-effective algorithm, namely the Continuous KNN algorithm, to overcome these limitations. Comprehensive experiments are conducted to demonstrate the efficiency of the proposed approach.

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References

  1. 1.
    Huang, Y.-K., Chen, C.-C., Lee, C.: Continuous k-nearest neighbor query for moving objects with uncertain velocity. GeoInformatica (accepted) (to appear)Google Scholar
  2. 2.
    Raptopoulou, K., Papadopoulos, A., Manolopoulos, Y.: Fast nearest-neighbor query processing in moving-object databases. GeoInformatica 7(2), 113–137 (2003)CrossRefGoogle Scholar
  3. 3.
    Yu, X., Pu, K.Q., Koudas, N.: Monitoring k-nearest neighbor queries over moving objects. In: Proceedings of the International Conference on Data Engineering (2005)Google Scholar
  4. 4.
    Cho, H.-J., Chung, C.-W.: An efficient and scalable approach to cnn queries in a road network. In: Proceedings of the International Conference on Very Large Data Bases, Trondheim, Norway (2005)Google Scholar
  5. 5.
    Jensen, C.S., Kolar, J., Pedersen, T.B., Timko, I.: Nearest neighbor queries in road networks. In: Proceedings of the ACM GIS, New Orleans, Louisiana, USA, November 7-8 (2003)Google Scholar
  6. 6.
    Kolahdouzan, M., Shahabi, C.: Continuous k nearest neighbor queries in spatial network databases. In: Proceedings of the Spatio-Temporal Databases Management (STDBM), Toronto, Canada, August 30 (2004)Google Scholar
  7. 7.
    Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: Proceedings of the International Conference on Very Large Data Bases, Berlin, Germany, September 9-12 (2003)Google Scholar
  8. 8.
    Mouratidis, K., Yiu, M.L., Papadias, D., Mamoulis, N.: Continuous nearest neighbor monitoring in road networks. In: Proceedings of the International Conference on Very Large Data Bases, Seoul, Korea, September 12-15 (2006)Google Scholar
  9. 9.
    Dijkstra, E.W.: A note on two problems in connection with graphs. Numerische Mathematik 1, 269–271 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Kung, R., Hanson, E., Ioannidis, Y., Sellis, T., Shapiro, L., Stonebraker, M.: Heuristic search in data base system. In: Proceedings of the International Workshop on Expert Database Systems (1986)Google Scholar
  11. 11.
    Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: Proceedings of the International Conference on Very Large Data Bases, Toronto, Canada (2004)Google Scholar
  12. 12.
  13. 13.
    Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Yuan-Ko Huang
    • 1
  • Zhi-Wei Chen
    • 1
  • Chiang Lee
    • 1
  1. 1.Department of Computer Science and Information EngineeringNational Cheng-Kung UniversityTainanTaiwan, R.O.C.

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