Exploiting Spatial Autocorrelation to Efficiently Process Correlation-Based Similarity Queries

  • Pusheng Zhang
  • Yan Huang
  • Shashi Shekhar
  • Vipin Kumar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2750)


A spatial time series dataset is a collection of time series, each referencing a location in a common spatial framework. Correlation analysis is often used to identify pairs of potentially interacting elements from the cross product of two spatial time series datasets (the two datasets may be the same). However, the computational cost of correlation analysis is very high when the dimension of the time series and the number of locations in the spatial frameworks are large. In this paper, we use a spatial autocorrelation-based search tree structure to propose new processing strategies for correlation-based similarity range queries and similarity joins. We provide a preliminary evaluation of the proposed strategies using algebraic cost models and experimental studies with Earth science datasets.


Spatial Autocorrelation Query Processing Search Tree Range Query Selectivity Ratio 
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.
    NOAA El Nino Page,
  2. 2.
    Agrawal, R., Faloutsos, C., Swami, A.: Efficient Similarity Search In Sequence Databases. In: Proc. of the 4th Int’l Conference of Foundations of Data Organization and Algorithms (1993)Google Scholar
  3. 3.
    Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vitter, J.: Scalable Sweeping- Based Spatial Join. In: Proc. of the 24th Int’l. Conf. on VLDB (1998)Google Scholar
  4. 4.
    Box, G., Jenkins, G., Reinsel, G.: Time Series Analysis: Forecasting and Control. Prentice Hall, Englewood Cliffs (1994)zbMATHGoogle Scholar
  5. 5.
    Lindgren, B.W.: Statistical Theory, 4th edn. Chapman-Hall, Boca Raton (1998)Google Scholar
  6. 6.
    Chan, K., Fu, A.W.: Efficient Time Series Matching by Wavelets. In: Proc. of the 15th ICDE (1999)Google Scholar
  7. 7.
    Cressie, N.: Statistics for Spatial Data. John Wiley and Sons, Chichester (1991)zbMATHGoogle Scholar
  8. 8.
    Elmasri, R., Navathe, S.: Fundamentals of Database Systems. Addsion Wesley Higher Education, London (2002)zbMATHGoogle Scholar
  9. 9.
    Faloutsos, C.: Searching Multimedia Databases By Content. Kluwer Academic Publishers, Dordrecht (1996)zbMATHGoogle Scholar
  10. 10.
    Food and Agriculture Organization. Farmers brace for extreme weather conditions as El Nino effect hits Latin America and Australia,
  11. 11.
    Grossman, R., Kamath, C., Kegelmeyer, P., Kumar, V., Namburu, R. (eds.): Data Mining for Scientific and Engineering Applications. Kluwer Academic Publishers, Dordrecht (2001) ISBN: 1-4020-0033-2Google Scholar
  12. 12.
    Gunopulos, D., Das, G.: Time Series Similarity Measures and Time Series Indexing. SIGMOD Record 30(2) (2001)Google Scholar
  13. 13.
    DeWitt, D.J., Patel, J.M.: Partition BasedS patial-Merge Join. In: Proc. of the ACM SIGMOD Conference (1996)Google Scholar
  14. 14.
    Leutenegger, S.T., Lopez, M.A.: The Effect of Buffering on the Performance of R-Trees. In: Proc. of the ICDE Conf., pp. 164–171 (1998)Google Scholar
  15. 15.
    Potter, C., Klooster, S., Brooks, V.: Inter-annual Variability in Terrestrial Net Primary Production: Exploration of Trends and Controls on Regional to Global Scales. Ecosystems 2(1), 36–48 (1999)CrossRefGoogle Scholar
  16. 16.
    Rigaux, P., Scholl, M., Voisard, A.: Spatial Databases: With Application to GIS. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  17. 17.
    Roddick, J., Hornsby, K., Spiliopoulou, M.: An Updated Bibliography of Temporal, Spatial, and Spatio-Temporal Data Mining Research. In: Roddick, J., Hornsby, K.S. (eds.) TSDM 2000. LNCS (LNAI), vol. 2007, p. 147. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  18. 18.
    Samet, H.: The Design and Analysis of Spatial Data Structures. Addison-Wesley Publishing Company, Reading (1990)Google Scholar
  19. 19.
    Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Prentice Hall, Englewood Cliffs (2003) ISBN:0130174807Google Scholar
  20. 20.
    Shekhar, S., Chawla, S., Ravada, S., Fetterer, A., Liu, X., Lu, C.T.: Spatial Databases: Accomplishments and Research Needs. IEEE TKDE 11(1) (1999)Google Scholar
  21. 21.
    Tobler, W.R.: Cellular Geography, Philosophy in Geography. In: Gale, S., Olsson, C. (eds.) Cellular Geography, Philosophy in Geography, Reidel, Dordrecht (1979)Google Scholar
  22. 22.
    Worboys, M.F.: GIS - A Computing Perspective. Taylor and Francis, Abington (1995)Google Scholar
  23. 23.
    Zhang, P., Huang, Y., Shekhar, S., Kumar, V.: Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach. In: Proc. of the 7th Pacific-Asia Conf. on Knowledge Discovery and Data Mining (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Pusheng Zhang
    • 1
  • Yan Huang
    • 1
  • Shashi Shekhar
    • 1
  • Vipin Kumar
    • 1
  1. 1.Computer Science & Engineering DepartmentUniversity of MinnesotaMinneapolisUSA

Personalised recommendations