Predictive Nearest Neighbor Queries over Uncertain Spatial-Temporal Data

  • Jinghua Zhu
  • Xue Wang
  • Yingshu Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8491)


Predictive nearest neighbor queries over spatial-temporal data have received significant attention in many location-based services including intelligent transportation, ride sharing and advertising. Due to physical and resource limitations of data collection devices like RFID, sensors and GPS, data is collected only at discrete time instants. In-between these discrete time instants, the positions of the monitored moving objects are uncertain. In this paper, we exploit the filtering and refining framework to solve the predictive nearest neighbor queries over uncertain spatial-temporal data. Specifically, in the filter phase, our approach employs a semi-Markov process model that describes object mobility between space grids and prunes those objects that have zero probability to encounter the queried object. In the refining phase, we use a Markov chain model to describe the mobility of moving objects between space points and compute the nearest neighbor probability for each candidate object. We experimentally show that our approach can filter out most of the impossible objects and has a good predication performance.


spatial-temporal data uncertain data predictive query Markov chain semi-Markov model 


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  1. 1.
    Hu, H., Xu, J., Lee, D.L.: A Generic Framework for Monitoring Continuous Spatial Queries over Moving Objects. In: Proceedings of the ACM International Conference on Management of Data, SIGMOD, Maryland, USA, pp. 479–490 (June 2005)Google Scholar
  2. 2.
    Hendawi, A.M., Mokbel, M.F.: Panda: A Predictive Spatio-Temporal Query Processor. In: Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM SIGSPATIAL GIS, California (2012)Google Scholar
  3. 3.
    Jeung, H., Liu, Q., Shen, H.T., Zhou, X.: A Hybrid Prediction Model for Moving Objects. In: Proceedings of the International Conference on Data Engineering, ICDE, Cancun, Mexico, pp. 70–79 (April 2008)Google Scholar
  4. 4.
    Niedermayer, J., Zufle, A., Emrich, T., Renz, M., Mamoulis, N., Chen, L., Kriegel, H.-P.: Probabilistic nearest neighbor queries on uncertain moving object trajectories. To Appear in the 40th International Conference on Very Large Databases, VLDB (2014)Google Scholar
  5. 5.
    Bian, K., Park, J.M., Chen, R.: A quorum-based framework for establishing control channels in dynamic spectrum access networks. In: MOBICOM, pp. 25–36 (2009)Google Scholar
  6. 6.
    Emrich, T., Kriegel, H.-P., Mamoulis, N., Renz, M., Zufle, A.: Indexing uncertain spatio-temporal data. In: Proc. CIKM, pp. 395–404 (2012)Google Scholar
  7. 7.
    Zhang, R., Jagadish, H.V., Dai, B.T., Ramamohanarao, K.: Optimized Algorithms for Predictive Range and KNN Queries on Moving Objects. Information Systems 35(8), 911–932 (2010)CrossRefGoogle Scholar
  8. 8.
    Sun, J., Papadias, D., Tao, Y., Liu, B.: Querying about the Past, the Present, and the Future in Spatio-Temporal. In: Proceedings of the International Conference on Data Engineering, ICDE, Massachusetts, USA, pp. 202–213 (March 2004)Google Scholar
  9. 9.
    Zhang, M., Chen, S., Jensen, C.S., Ooi, B.C., Zhang, Z.: Effectively Indexing Uncertain Moving Objects for Predictive Queries. PVLDB 2(1), 1198–1209 (2009)Google Scholar
  10. 10.
    Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: 0002. Prediction and Indexing of Moving Objects with Unknown Motion Patterns. In: Proceedings of the ACM International Conference on Management of Data, SIGMOD, Paris, France, pp. 611–622 (June 2004)Google Scholar
  11. 11.
    Cheng, S., Li, J., Cai, Z.: O(ε)-Approximation to Physical World by Sensor Networks. In: The 32rd Annual IEEE International Conference on Computer Communications, pp. 3084–3092Google Scholar
  12. 12.
    Cai, Z., Lin, G., Xue, G.: Improved Approximation Algorithms for the Capacitated Multicast Routing Problem. In: Wang, L. (ed.) COCOON 2005. LNCS, vol. 3595, pp. 136–145. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Ai, C., Guo, L., Cai, Z., Li, Y.: Processing Area Queries in Wireless Sensor Networks. In: The Fifth International Conference on Mobile Ad-hoc and Sensor NetworksGoogle Scholar
  14. 14.
    Wang, X., Guo, L., Ai, C., Li, J., Cai, Z.: An Urban Area-Oriented Traffic Information Query Strategy in VANETs. In: Ren, K., Liu, X., Liang, W., Xu, M., Jia, X., Xing, K. (eds.) WASA 2013. LNCS, vol. 7992, pp. 313–324. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Cai, Z., Chen, Z., Lin, G.: A 3.4713-Approximation Algorithm for Capacicated Multicast Tree Routing Problem. Theoretial Computer Science 410(52), 5415–5424 (2009)CrossRefzbMATHMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jinghua Zhu
    • 1
  • Xue Wang
    • 1
  • Yingshu Li
    • 1
  1. 1.Department of Computer ScienceGeorgia State UniversityAtlantaUSA

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