Evaluation of a Dynamic Tree Structure for Indexing Query Regions on Streaming Geospatial Data

  • Quinn Hart
  • Michael Gertz
  • Jie Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3633)

Abstract

Most recent research on querying and managing data streams has concentrated on traditional data models where the data come in the form of tuples or XML data. Complex types of streaming data, in particular spatio-temporal data, have primarily been investigated in the context of moving objects and location-aware services. In this paper, we study query processing and optimization aspects for streaming (RSI) data. Streaming RSI is typical for the vast amount of imaging satellites orbiting the Earth, and it exhibits certain characteristics that make it very attractive to tailored query optimization techniques. Our approach uses a Dynamic Cascade Tree (DCT) to (1) index spatio-temporal query regions associated with continuous user queries and (2) efficiently determine what incoming RSI data is relevant to what queries. The (DCT) supports the processing of different types of RSI data, ranging from point data to more general spatial extents in which the incoming imagery can be single pixels, rows of pixels, or discrete parts of images. The DCT exploits spatial trends in incoming RSI data to efficiently filter the data of interest to the individual query regions. Experimental results using random input and Geostationary Operational Environmental Satellite (GOES) data give a good insight into processing streaming RSI and verify the efficiency and utility of the DCT .

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References

  1. 1.
    Abadi, D., Carney, D., Cetintemel, U., Cherniack, M., Convey, C., Lee, S., Stonebraker, M., Tatbul, N., Zdonik, S.: Aurora: A new model and architecture for data stream management. The VLDB Journal 12(2), 120–139 (2003)CrossRefGoogle Scholar
  2. 2.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and issues in data stream systems. In: Proc. 21th ACM Symposium on Principles of Database Systems (PODS), pp. 1–16 (2002)Google Scholar
  3. 3.
    Babcock, B., Babu, S., Datar, M., Motwani, R., Thomas, D.: Operator scheduling in data stream systems. The VLDB Journal 13(4), 335–353 (2004)CrossRefGoogle Scholar
  4. 4.
    de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications. Springer, Heidelberg (2000)MATHGoogle Scholar
  5. 5.
    Carney, D., Cetintemel, U., Rasin, A., Zdonik, S., Cherniack, M., Stonebraker, M.: Operator scheduling in a data stream manager. In: Proceedings of 29th VLDB Conference, pp. 838–849 (2003)Google Scholar
  6. 6.
    Chandrasekaran, S., Cooper, O., Deshpande, A., Franklin, M.J., Hellerstein, J.M., Hong, W., Krishnamurthy, S., Madden, S., Raman, V., Reiss, F., Shah, M.A.: TelegraphCQ: Continuous dataflow processing for an uncertain world. In: First Biennial Conference on Innovative Data Systems Research, CIDR 2003 (2003)Google Scholar
  7. 7.
    GOES I-M DataBook. Space Systems-Loral (2001), rsd.gsfc.nasa.gov/goes/text/goes.databook.html
  8. 8.
    Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proceedings of the ACM SIGMOD Int. Conf. on Management of Data, pp. 47–57 (1984)Google Scholar
  9. 9.
    Hadjieleftheriou, M.: Spatial Index Library. Department of Computer Science and Engineering, University of California, Riverside, version 0.80b edn. (2004)Google Scholar
  10. 10.
    Hart, Q., Gertz, M.: Indexing query regions for streaming geospatial data. In: 2nd Workshop on Spatio-temporal Database Management, STDBM 2004 (2004)Google Scholar
  11. 11.
    Hellerstein, J.M., Franklin, M.J., Chandrasekaran, S., Deshpande, A., Hildrum, K., Madden, S., Raman, V., Shah, M.A.: Adaptive query processing: Technology in evolution. IEEE Data Eng. Bulletin 23, 7–18 (2000)Google Scholar
  12. 12.
    Kalashnikov, D.V., Prabhakar, S., Hambrusch, S.E.: Main memory evaluation of monitoring queries over moving objects. Distributed and Parallel Databases 15(2), 117–135 (2004)CrossRefGoogle Scholar
  13. 13.
    Kim, K., Cha, S.K., Kwon, K.: Optimizing multidimensional index trees for main memory access. In: Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 139–150 (2001)Google Scholar
  14. 14.
    van Kreveld, M.J., Overmars, M.K.: Concatenable segment trees. Technical report, Rijksuniversiteit Utrecht (1988)Google Scholar
  15. 15.
    Madden, S., Shah, M., Hellerstein, J.M., Raman, V.: Continuously adaptive continuous queries over streams. In: Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 49–60 (2002)Google Scholar
  16. 16.
    Madden, S., Franklin, M.J.: Fjording the stream: An architecture for queries over streaming sensor data. In: Intern. Conf. on Data Engineering, pp. 555–566 (2002)Google Scholar
  17. 17.
    Manolopoulos, Y., Nanopoulos, A., Papadopoulos, A.N., Theodoridis, Y.: R-trees have grown everywhere. Unpublished Technical Report (2003), http://www.rtreeportal.org/pubs/MNPT03.pdf
  18. 18.
    Mokbel, M.F., Xiong, X., Aref, W.G.: SINA: Scalable incremental processing of continuous queries in spatio-temporal databases. In: Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 623–634 (2004)Google Scholar
  19. 19.
    Prabhakar, S., Xia, Y., Kalashnikov, D., Aref, W., Hambrusch, S.: Query indexing and velocity constrained indexing: Scalable techniques for continuous queries on moving objects. IEEE Trans. on Computers 51(10), 1124–1140 (2002)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Samet, H.: Hierarchical representations of collections of small rectangles. ACM Comput. Surv. 20(4), 271–309 (1988)MATHCrossRefMathSciNetGoogle Scholar
  21. 21.
    Sellis, T.K., Roussopoulos, N., Faloutsos, C.: The R+-tree: A dynamic index for multi-dimensional objects. In: Proceedings of 13th International Conference on Very Large Data Bases, pp. 507–518 (1987)Google Scholar
  22. 22.
    SGI: Standard Template Library Programmer’s Guide (1999)Google Scholar
  23. 23.
    Zhang, J., Zhu, M., Papadias, D., Tao, Y., Lee, D.L.: Location-based spatial queries. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 443–454 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Quinn Hart
    • 1
  • Michael Gertz
    • 2
  • Jie Zhang
    • 2
  1. 1.CalSpaceUniversity of CaliforniaDavisUSA
  2. 2.Dept. of Computer ScienceUniversity of CaliforniaDavisUSA

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