Advertisement

Probabilistic Spatial Database Operations

  • Jinfeng Ni
  • Chinya V. Ravishankar
  • Bir Bhanu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2750)

Abstract

Spatial databases typically assume that the positional attributes of spatial objects are precisely known. In practice, however, they are known only approximately, with the error depending on the nature of the measurement and the source of data. In this paper, we address the problem how to perform spatial database operations in the presence of uncertainty. We first discuss a probabilistic spatial data model to represent the positional uncertainty. We then present a method for performing the probabilistic spatial join operations, which, given two uncertain data sets, find all pairs of polygons whose probability of overlap is larger than a given threshold. This method uses an R-tree based probabilistic index structure (PrR-tree) to support probabilistic filtering, and an efficient algorithm to compute the intersection probability between two uncertain polygons for the refinement step. Our experiments show that our method achieves higher accuracy than methods based on traditional spatial joins, while reducing overall cost by a factor of more than two.

Keywords

Spatial Data Spatial Database Spatial Object Intersection Probability Candidate Pair 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aboulnaga, A., Naughton, J.F.: Accurate estimation of the cost of spatial selections. In: Proceedings of International Conference on Data Engineering (ICDE), San Diego, California, March 2000, pp. 123–134 (2000)Google Scholar
  2. 2.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: Proceedings of the 1984 ACM-SIGMOD Conference, Boston, Massachusetts, June 1984, pp. 47–57 (1984)Google Scholar
  3. 3.
    Azouzi, M.: Introducing the concept of reliability in spatial data. In: Lowell, K., Jaton, A. (eds.) Spatial Accuracy Assessment: land information uncertainty in Natural Resources, pp. 139–144. Ann Arbor Press (1999)Google Scholar
  4. 4.
    Brinkhoff, T., Kriegel, H., Schneider, R., Seeger, B.: Multi-step processing of spatial joins. In: Proceedings of the 1994 ACM-SIGMOD Conference, Minneapolis, Minnesota, May 1994, pp. 197–208 (1994)Google Scholar
  5. 5.
    Brinkhoff, T., Kriegel, H., Seeger, B.: Efficient processing of spatial joins using r-trees. In: Proceedings of the 1993 ACM-SIGMOD Conference, Washington, D.C., May 1993, pp. 237–246 (1993)Google Scholar
  6. 6.
    FGDC. National standard for spatial data accuracy, fgdc-std-007.3-1998 (March 1998), http://www.fgdc.gov/standards/status/sub13.html
  7. 7.
    Goodchild, M.F., Shortridge, A.M., Fohl, P.: Encapsulating simulation models with geospatial data sets. In: Lowell, K., Jaton, A. (eds.) Spatial Accuracy Assessment: land information uncertainty in Natural Resources, pp. 123–129. Ann Arbor Press (1999)Google Scholar
  8. 8.
    Heuvelink, G.B.M.: Error Propagation in Environmental Modeling with GIS. Taylor & Francis, London (1998)Google Scholar
  9. 9.
    Huang, Y.W., Jones, M., Rundensteiner, E.A.: Symbolic intersect detection: A method for improving spatial intersect joins. GeoInformatica 2(2), 149–174 (1998)CrossRefGoogle Scholar
  10. 10.
    Hunter, G., Goodchild, M.F.: Application of new model of vector data uncertainty. In: Lowell, K., Jaton, A. (eds.) Spatial Accuracy Assessment: land information uncertainty in Natural Resources, pp. 203–208. Ann Arbor Press (1999)Google Scholar
  11. 11.
    King, J.P.: Modeling boundaries of influence among positional uncertainty fields. Master’s thesis, University of Maine (December 2002)Google Scholar
  12. 12.
    Koudas, N., Sevcik, K.C.: Size separation spatial join. In: Proceedings of the 1997 ACM-SIGMOD Conference, Tucson, Arizona, May 1997, pp. 324–335 (1997)Google Scholar
  13. 13.
    Leung, Y., Yan, J.: Point-in-polygon analysis under certainty and uncertainty. GeoInformatica 1(1), 93–114 (1997)CrossRefGoogle Scholar
  14. 14.
    Leung, Y., Yan, J.: A locational error model for spatial features. Int. J. Geographical Information Science 12(6), 607–620 (1998)CrossRefGoogle Scholar
  15. 15.
    Lo, M.L., Ravishankar, C.V.: Spatial joins using seeded trees. In: Proceedings of the 1994 ACM-SIGMOD Conference, Minneapolis, Minnesota, June 1994, pp. 209–220 (1994)Google Scholar
  16. 16.
    Lo, M.L., Ravishankar, C.V.: Spatial hash join. In: Proceedings of the 1996 ACM-SIGMOD Conference, Montreal, Canada, June 1996, pp. 247–258 (1996)Google Scholar
  17. 17.
    Mamoulis, N., Papadias, D.: Slot index spatial join. IEEE Transaction on Knowledge and Data Engineering 15(1), 211–231 (2003)CrossRefGoogle Scholar
  18. 18.
    Minnesota Planning. Positional accuracy handbook (October 1999), http://www.mnplan.state.mn.us/press/accurate.html
  19. 19.
    U.S. Bureau of the Census. Census 2000 TIGER/Line Data. Washington DC (2000) Google Scholar
  20. 20.
    Balakrishnan, N., Rao, C.R.: Order statistics: theory and methods. Elsevier, New York (1998)Google Scholar
  21. 21.
    Beckmann, N., Schneider, R., Kriegel, H.P., Seeger, B.: The r*-tree: An efficient and robust access method for points and rectangles. In: Proceedings of the 1990 ACM-SIGMOD Conference, Atlantic City, NJ, May 1990, pp. 322–331 (1990)Google Scholar
  22. 22.
    Openshaw, S.: Learning to live with errors in spatial databases. In: Goodchild, M., Gopal, S. (eds.) Accuracy of Spatial Databases, pp. 263–276. Taylor & Francis, Abington (1989)Google Scholar
  23. 23.
    Patel, J.M., DeWitt, D.J.: Partition based spatial-merge join. In: Proceedings of the 1996 ACM-SIGMOD Conference, Montreal, Canada, June 1996, pp. 259–270 (1996)Google Scholar
  24. 24.
    Pfoser, D., Jensen, C.S.: Capturing the uncertainty of moving-object representations. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651, pp. 111–132. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  25. 25.
    Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Querying imprecise data in moving object environments. In: Poster Session, International Conference on Data Engineering(ICDE) (2003) (to appear)Google Scholar
  26. 26.
    Shi, W.: A generic statistical approach for modeling error of geometric features in gis. Int. J. Geographical Information Science 12(2), 131–143 (1998)CrossRefGoogle Scholar
  27. 27.
    Shi, W., Liu, W.: A stochastic process-based model for the positional error of line segments in gis. Int. J. Geographical Information Science 14(1), 51–66 (2000)CrossRefGoogle Scholar
  28. 28.
    Trajcevski, G., Wolfson, O., Zhang, F., Chamberlain, S.: The geometry of uncertainty in moving object databases. In: Proceedings of the 8th International Conference on Extending Database Technology(EDBT), Prague, Czech Republic, March 2002, pp. 233–250 (2002)Google Scholar
  29. 29.
    Wolfson, O., Sistla, P.A., Chamberlain, S., Yesha, Y.: Updating and querying databases that track mobile units. Distributed and Parallel Databases 3(7), 257–387 (1999)CrossRefGoogle Scholar
  30. 30.
    Zhang, J.X., Goodchild, M.F.: Uncertainty in Geographical Information System. Taylor & Francis, Erewhon (2002)CrossRefGoogle Scholar
  31. 31.
    Zimbrao, G., Souza, J.M.: A raster approximation for the processing of spatial joins. In: Proceedings of the 24th VLDB Conference, New York City, New York, August 1998, pp. 311–322 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jinfeng Ni
    • 1
  • Chinya V. Ravishankar
    • 1
  • Bir Bhanu
    • 2
  1. 1.Department of Computer Science & Engineering 
  2. 2.Department of Electrical EngineeringUniversity of California, RiversideRiversideUSA

Personalised recommendations