Speed Partitioning for Indexing Moving Objects

  • Xiaofeng XuEmail author
  • Li Xiong
  • Vaidy Sunderam
  • Jinfei Liu
  • Jun Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9239)


Indexing moving objects has been extensively studied in the past decades. Moving objects, such as vehicles and mobile device users, usually exhibit some patterns on their velocities, which can be utilized for velocity-based partitioning to improve performance of the indexes. Existing velocity-based partitioning techniques rely on some kinds of heuristics rather than analytically calculate the optimal solution. In this paper, we propose a novel speed partitioning technique based on a formal analysis over speed values of the moving objects. We first formulate the optimal speed partitioning problem based on search space expansion analysis and then compute the optimal solution using dynamic programming. We then build the partitioned indexing system where queries are duplicated and processed in each index partition. Extensive experiments demonstrate that our method dramatically improves the performance of indexes for moving objects and outperforms other state-of-the-art velocity-based partitioning approaches.



This research is supported by the AFOSR DDDAS program (Grant No. FA9550-12-1-0240) and National Natural Science Foundation of China (Grant No. 11271351).


  1. 1.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The r*-tree: an efficient and robust access method for points and rectangles. In: SIGMOD, pp. 322–331 (1990)Google Scholar
  2. 2.
    Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2), 153–180 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Chen, S., Jensen, C.S., Lin, D.: A benchmark for evaluating moving object indexes. PVLDB 1(2), 1574–1585 (2008)Google Scholar
  4. 4.
    Chen, S., Ooi, B.C., Tan, K.-L., Nascimento, M.A.: St\(^{\text{2 }}\)b-tree: a self-tunable spatio-temporal b\(^{\text{+ }}\)-tree index for moving objects. In: SIGMOD, pages 29–42, 2008Google Scholar
  5. 5.
    Dittrich, J., Blunschi, L., Vaz Salles, M.A.: Indexing moving objects using short-lived throwaway indexes. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds.) SSTD 2009. LNCS, vol. 5644, pp. 189–207. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  6. 6.
    Finkel, R.A., Bentley, J.L.: Quad trees: a data structure for retrieval on composite keys. Acta Inf. 4, 1–9 (1974)zbMATHCrossRefGoogle Scholar
  7. 7.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: SIGMOD, pp. 47–57 (1984)Google Scholar
  8. 8.
    Jensen, C.S., Lin, D., Ooi, B.C.: Query and update efficient b\(^{\text{+ }}\)-tree based indexing of moving objects. In: VLDB, pp. 768–779 (2004)Google Scholar
  9. 9.
    Jensen, C.S., Lu, H., Yang, B.: Indexing the trajectories of moving objects in symbolic indoor space. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds.) SSTD 2009. LNCS, vol. 5644, pp. 208–227. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  10. 10.
    Jensen, C.S., Pakalnis, S.: Trax - real-world tracking of moving objects. In: VLDB, pp. 1362–1365 (2007)Google Scholar
  11. 11.
    Nascimento, M.A., Silva, J.R.O., Theodoridis, Y.: Evaluation of access structures for discretely moving points. In: Böhlen, M.H., Jensen, C.S., Scholl, M.O. (eds.) STDBM 1999. LNCS, vol. 1678, pp. 171–188. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  12. 12.
    Nguyen, T., He, Z., Zhang, R., Ward, P.: Boosting moving object indexing through velocity partitioning. PVLDB 5(9), 860–871 (2012)Google Scholar
  13. 13.
    Patel, J.M., Chen, Y., Chakka, V.P.: Stripes: an efficient index for predicted trajectories. In: SIGMOD, pp. 637–646 (2004)Google Scholar
  14. 14.
    Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: SIGMOD, pp. 331–342 (2000)Google Scholar
  15. 15.
    Schiller, J., Voisard, A.: Location-Based Services. Elsevier, Amsterdam (2004) Google Scholar
  16. 16.
    Sidlauskas, D., Saltenis, S., Christiansen, C.W., Johansen, J.M., Saulys, D.: Trees or grids?: indexing moving objects in main memory. In: SIGSPATIAL, pp. 236–245 (2009)Google Scholar
  17. 17.
    Sidlauskas, D., Saltenis, S., Jensen, C.S.: Parallel main-memory indexing for moving-object query and update workloads. In: SIGMOD, pp. 37–48 (2012)Google Scholar
  18. 18.
    Sidlauskas, D., Saltenis, S., Jensen, C.S.: Processing of extreme moving-object update and query workloads in main memory. VLDB J. 23(5), 817–841 (2014)CrossRefGoogle Scholar
  19. 19.
    Silva, Y.N., Xiong, X., Aref, W.G.: The rum-tree: supporting frequent updates in r-trees using memos. VLDB J. 18(3), 719–738 (2009)CrossRefGoogle Scholar
  20. 20.
    Sistla, A.P., Wolfson, O., Chamberlain, S., Dao, S.: Modeling and querying moving objects. In: ICDE, pp. 422–432 (1997)Google Scholar
  21. 21.
    Tao, Y., Papadias, D., Sun, J.: The tpr*-tree: an optimized spatio-temporal access method for predictive queries. In: VLDB, pp. 790–801 (2003)Google Scholar
  22. 22.
    Yiu, M.L., Tao, Y., Mamoulis, N.: The b\(^{ \text{ dual }}\)-tree: indexing moving objects by space filling curves in the dual space. VLDB J. 17(3), 379–400 (2008)CrossRefGoogle Scholar
  23. 23.
    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
  24. 24.
    Zhu, Y., Wang, S., Zhou, X., Zhang, Y.: RUM+-tree: a new multidimensional index supporting frequent updates. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds.) WAIM 2013. LNCS, vol. 7923, pp. 235–240. Springer, Heidelberg (2013) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xiaofeng Xu
    • 1
    Email author
  • Li Xiong
    • 1
  • Vaidy Sunderam
    • 1
  • Jinfei Liu
    • 1
  • Jun Luo
    • 2
    • 3
  1. 1.Department of Mathematics/Computer ScienceEmory UniversityAtlantaUSA
  2. 2.HK Advanced Technology Center and Ecosystem Cloud Service Group, LenovoHong KongChina
  3. 3.Shenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina

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