Forecasting Region Search of Moving Objects

  • Jun Feng
  • Linyan Wu
  • Yuelong Zhu
  • Toyohide Watanabe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


The issue of how to provide location-based service (LBS) is attracted many researchers. In this paper, we focus on an useful searching type in LBS, forecasting search, which provides search result for a future time based on the current information of moving objects. To deal with such kind of searches, a PV-graph is proposed for analyzing the possible situations of the moving objects. By using PV-graph, forecasting queries in LBS applications can be responded efficiently.


Region Search Query Region Forecast Region Move Object Database Move Query Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Shahabi, C., Kolahdouzan, M.R., Sharifzadeh, M.: A road network embedding technique for k-nearest neighbor search in moving object databases. GeoInformatica (3), 255–273 (2003)Google Scholar
  2. 2.
    Kolahdouzan, M.R., Shahabi, C.: Continuous k nearest neighbor queries in spatial network databases. In: Proceedings of the Second Workshop on Spatio-Temporal Database Management, pp. 33–40 (2004)Google Scholar
  3. 3.
    Song, Z.X., Roussopoulos, N.: K-nearest neighbor search for moving query point. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 79–96. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Feng, J., Mukai, N., Watanabe, T.: Stepwise optimization method for k-cnn search for location-based service. In: Vojtáš, P., Bieliková, M., Charron-Bost, B., Sýkora, O. (eds.) SOFSEM 2005. LNCS, vol. 3381, pp. 363–366. Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Feng, J., Mukai, N., Watanabe, T.: Search on transportation network for location-based service. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS (LNAI), vol. 3533, pp. 657–666. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: Proc. of ACM SIGMOD 1984, pp. 47–57 (1984)Google Scholar
  7. 7.
    Tao, Y.F., Papadias, D., Sun, J.M.: The tpr*-tree: An optimized spatio-temporal access method for predictive queries. In: Aberer, K., Koubarakis, M., Kalogeraki, V. (eds.) VLDB 2003. LNCS, vol. 2944, pp. 790–801. Springer, Heidelberg (2004)Google Scholar
  8. 8.
    Lin, D., Jensen, C.S., Ooi, B.C., S̃altenis, S.: Efficient indexing of the historical, present, and future positions of moving objects. In: MEM 2005: Proceedings of the 6th international conference on Mobile data management, pp. 59–66. ACM Press, New York (2005)CrossRefGoogle Scholar
  9. 9.
    Patel, J.M., Chen, Y., Chakka, V.P.: Stripes: an efficient index for predicted trajectories. In: SIGMOD 2004: Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp. 635–646. ACM Press, New York (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Feng
    • 1
  • Linyan Wu
    • 1
  • Yuelong Zhu
    • 1
  • Toyohide Watanabe
    • 2
  1. 1.Hohai UniversityNanjingChina
  2. 2.Nagoya UniversityNagoyaJapan

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