Skip to main content
SpringerLink
Log in

Decomposition tree: a spatio-temporal indexing method for movement big data

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Movement is a complex process that evolves through both space and time. Movement data generated by moving objects is a kind of big data, which has been a focus of research in science, technology, economics, and social studies. Movement database is also at the forefront of geographic information science research. Developing efficient access methods for movement data stored in movement databases is of critical importance. Tree-like indexing structures such as the R-tree, Quadtree, Octree are not suitable for indexing multi-dimensional movement data because they all have high space cost of their inner nodes. In addition, it is difficult to use them for parallel access to multi-dimensional movement data because they thereof, are in hierarchical structures, which have severe overlapping problems in high dimensional space. In this paper, we propose a novel access method, the Decomposition Tree (D-tree), for indexing multi-dimensional movement data. The D-tree is a virtual tree without inner nodes, instead, through an encoding method based on integer bit-shifting operation, and can efficiently answer a wide range of queries. Experimental results show that the space cost and query performance of D-tree are superior to its best known competitors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Guo, H., et al.: Scientific big data and digital Earth. Chin. Sci. Bull. 59(35), 5066–5073 (2014)

    Article  Google Scholar 

  2. Long, J.A., Nelson, T.A.: A review of quantitative methods for movement data. Int. J. Geogr. Inf. Sci. 45, 1–27 (2012)

    Google Scholar 

  3. Wang, L., et al.: Cloud computing: a perspective study. New Gener. Comput. 28(2), 137–146 (2010)

    Article  Google Scholar 

  4. Wang, L., et al.: Towards enabling cyberinfrastructure as a service in clouds. Comput. Electr. Eng. 39(1), 3–14 (2013)

    Article  Google Scholar 

  5. Chen, D., et al.: Fast and scalable multi-way analysis of massive neural data. IEEE Trans. Comput. 64(3), 707–719 (2015)

    Article  MathSciNet  Google Scholar 

  6. Wang, L., et al.: Particle swarm optimization based dictionary learning for remote sensing big data. Knowl. Based Syst. 79, 43–50 (2015)

    Article  Google Scholar 

  7. Saltenis, S., et al.: Indexing the positions of continuously moving objects. SIGMOD Rec. 29(2), 331–342 (2000)

    Article  Google Scholar 

  8. Lin, B., Su, J.W.: On bulk loading TPR-tree. In: 2004 IEEE International Conference on Mobile Data Management. pp. 114–124 (2004)

  9. Tao, Y., et al., The TPR*-Tree: An Optimized Spatio-Temporal Access Method for Predictive Queries. In: Proceedings 2003 VLDB Conference, pp. 790–801. Morgan Kaufmann, San Francisco (2003)

  10. Kim, S.W., Jang, M.H., Lim, S.: Active adjustment: an effective method for keeping the TPR*-tree compact. J. Inf. Sci. Eng. 26(5), 1583–1600 (2010)

    Google Scholar 

  11. Christian, S.J., Dan, L., Beng Chin, O.: Query and update efficient B\(^{+}\)-tree based indexing of moving objects. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, vol. 30. VLDB Endowment, Toronto (2004)

  12. Xiaopeng, X., Mohamed, F.M., Walid, G.A.: LUGrid: update-tolerant grid-based indexing for moving objects. In: Proceedings of the 7th International Conference on Mobile Data Management. IEEE Computer Society (2006)

  13. Mindaugas, P., et al.: Indexing the past, present, and anticipated future positions of moving objects. ACM Trans. Database Syst. 31(1), 255–298 (2006)

    Article  Google Scholar 

  14. Yiu, M.L., Tao, Y.F., Mamoulis, N.: The B-dual-tree: indexing moving objects by space filling curves in the dual space. VLDB J. 17(3), 379–400 (2008)

    Article  Google Scholar 

  15. Chen, N., et al.: Adaptive indexing of moving objects with highly variable update frequencies. J. Comput. Sci. Technol. 23(6), 998–1014 (2008)

    Article  Google Scholar 

  16. Jensen, C., et al.: Indexing the Trajectories of Moving Objects in Symbolic Indoor Space Advances in Spatial and Temporal Databases, pp. 208–227. Springer, Berlin (2009)

    Google Scholar 

  17. Chen, N., et al.: B(S)-tree: a self-tuning index of moving objects, part II. In: Proceedings of Database Systems for Advanced Applications, pp. 1–16 (2010)

  18. Lin, H.Y., Indexing the trajectories of moving objects. In: Imecs 2009: International Multi-Conference of Engineers and Computer Scientists, vol. I, II, pp. 732–737. International Association Engineers-IAENG, Hong Kong (2009)

  19. Lin, H.Y.: Using compressed index structures for processing moving objects in large spatio-temporal databases. J. Syst. Softw. 85(1), 167–177 (2012)

    Article  Google Scholar 

  20. Bayer, R., McCreight, E.M.: Organization and maintenance of large ordered indexes. Acta Inf. 1(3), 173–189 (1972)

    Article  Google Scholar 

  21. Guttman, A.: R-trees: a dynamic index structure for spatial searching. SIGMOD Rec. 14(2), 47–57 (1984)

    Article  Google Scholar 

  22. Nguyen-Dinh, L.-V., Aref, W.G., Mokbel, M.F.: Spatio-temporal access methods: part 2 (2003–2010). Bulletin of the IEEE Computer Society Technical Committee on Data Engineering (2010)

  23. Norbert, B., et al.: The R*-tree: an efficient and robust access method for points and rectangles. SIGMOD Rec. 19(2), 322–331 (1990)

    Article  Google Scholar 

  24. Tao, Y., Papadias, D., Sun, J.: The TPR*-tree: an optimized spatio-temporal access method for predictive queries (2003)

  25. Lin, H.-Y.: Using B+-trees for processing of line segments in large spatial databases. J. Intell. Inf. Syst. 31(1), 35–52 (2008)

    Article  Google Scholar 

  26. Huang, M.L., Hu, P., Xia, L.F.: A grid based trajectory indexing method for moving objects on fixed network. In: 2010 18th International Conference on Geoinformatics (2010)

  27. Chen, H., et al.: Recent Trends in Wireless and Mobile Networks. A cyclic-translation-based grid-quadtree index for continuous range queries over moving objects, pp. 95–109. Springer, Berlin (2011)

    Book  Google Scholar 

  28. Nievergelt, J., Hans, H., Kenneth, C.S.: The grid file: an adaptable, symmetric multikey file structure. ACM Trans. Database Syst. 9(1), 38–71 (1984)

    Article  Google Scholar 

Download references

Acknowledgments

The work described in this paper was supported by National Natural Science Foundation of China (41101368, 41172300) and National High Technology Research and Development Program of China (2012AA121401).

Author information

Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, 388 Lumo Road, Wuhan, 430074, China

    Zhenwen He, Chonglong Wu, Gang Liu, Zufang Zheng & Yiping Tian

  2. Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430074, China

    Zhenwen He, Chonglong Wu, Gang Liu & Yiping Tian

  3. School of Industrial Design, Hubei University of Technology, Wuhan, 430068, China

    Zufang Zheng

Authors
  1. Zhenwen He
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chonglong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zufang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yiping Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gang Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, Z., Wu, C., Liu, G. et al. Decomposition tree: a spatio-temporal indexing method for movement big data. Cluster Comput 18, 1481–1492 (2015). https://doi.org/10.1007/s10586-015-0475-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-015-0475-3

Keywords

Search

Navigation