Evaluating Trajectory Queries over Imprecise Location Data

  • Xike Xie
  • Reynold Cheng
  • Man Lung Yiu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7338)


Trajectory queries, which retrieve nearby objects for every point of a given route, can be used to identify alerts of potential threats along a vessel route, or monitor the adjacent rescuers to a travel path. However, the locations of these objects (e.g., threats, succours) may not be precisely obtained due to hardware limitations of measuring devices, as well as the constantly-changing nature of the external environment. Ignoring data uncertainty can render low query quality, and cause undesirable consequences such as missing alerts of threats and poor response time in rescue operations. Also, the query is quite time-consuming, since all the points on the trajectory are considered. In this paper, we study how to efficiently evaluate trajectory queries over imprecise location data, by proposing a new concept called the u-bisector. In general, the u-bisector is an extension of bisector to handle imprecise data. Based on the u-bisector, we design several novel filters to make our solution scalable to a long trajectory and a large database size. An extensive experimental study on real datasets suggests that our proposal produces better results than traditional solutions that do not consider data imprecision.


Near Neighbor Query Point Error Score Neighbor Query Validity Interval 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tao, Y., Papadias, D., Shen, Q.: Continuous nearest neighbor search. In: VLDB (2002)Google Scholar
  2. 2.
    U. S. C. Guard, Announcement of 2011 international ice patrol services (2011),
  3. 3.
    Jesse, L., Janet, R., Edward, G., Lee, V.: Effects of habitat on gps collar performance: using data screening to reduce location error. Journal of Applied Ecology (2007)Google Scholar
  4. 4.
    Park, K., Choo, H., Valduriez, P.: A scalable energy-efficient continuous nearest neighbor search in wireless broadcast systems. In: Wireless Networks (2010)Google Scholar
  5. 5.
    Cheng, R., Xie, X., Yiu, M.L., Chen, J., Sun, L.: Uv-diagram: A voronoi diagram for uncertain data. In: ICDE (2010)Google Scholar
  6. 6.
    Lian, X., Chen, L.: Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data. VLDBJ (2009)Google Scholar
  7. 7.
    Cheema, M.A., Lin, X., Wang, W., Zhang, W., Pei, J.: Probabilistic reverse nearest neighbor queries on uncertain data. TKDE (2010)Google Scholar
  8. 8.
    Chen, J., Cheng, R., Mokbel, M., Chow, C.: Scalable processing of snapshot and continuous nearest-neighbor queries over one-dimensional uncertain data. VLDBJ (2009)Google Scholar
  9. 9.
    Trajcevski, G., Tamassia, R., Ding, H., Scheuermann, P., Cruz, I.F.: Continuous probabilistic nearest-neighbor queries for uncertain trajectories. In: EDBT, pp. 874–885 (2009)Google Scholar
  10. 10.
    Zheng, K., Fung, G.P.C., Zhou, X.: K-nearest neighbor search for fuzzy objects. In: SIGMOD (2010)Google Scholar
  11. 11.
    Song, Z., 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
  12. 12.
    Zheng, B., Lee, D.-L.: Semantic Caching in Location-Dependent Query Processing. In: Jensen, C.S., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol. 2121, pp. 97–113. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    Zhang, J., Zhu, M., Papadias, D., Tao, Y., Lee, D.L.: Location-based spatial queries. In: SIGMOD (2003)Google Scholar
  14. 14.
    Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Querying imprecise data in moving object environments. TKDE 16(9) (2004)Google Scholar
  15. 15.
    Boyd, S., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004)Google Scholar
  16. 16.
    Hadjieleftheriou, M.: Spatial index library version 0.44.2b,
  17. 17.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to data mining (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xike Xie
    • 1
  • Reynold Cheng
    • 2
  • Man Lung Yiu
    • 3
  1. 1.Aalborg UniversityDenmark
  2. 2.University of Hong KongHong Kong
  3. 3.Hong Kong Polytechnic UniversityHung HomHong Kong

Personalised recommendations