Skip to main content
SpringerLink
Log in

Probabilistic Spatial Database Operations

  • Conference paper
Advances in Spatial and Temporal Databases (SSTD 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2750))

Included in the following conference series:

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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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. 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. 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. 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. 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. FGDC. National standard for spatial data accuracy, fgdc-std-007.3-1998 (March 1998), http://www.fgdc.gov/standards/status/sub13.html

  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. Heuvelink, G.B.M.: Error Propagation in Environmental Modeling with GIS. Taylor & Francis, London (1998)

    Google Scholar 

  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)

    Article  Google Scholar 

  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. King, J.P.: Modeling boundaries of influence among positional uncertainty fields. Master’s thesis, University of Maine (December 2002)

    Google Scholar 

  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. Leung, Y., Yan, J.: Point-in-polygon analysis under certainty and uncertainty. GeoInformatica 1(1), 93–114 (1997)

    Article  Google Scholar 

  14. Leung, Y., Yan, J.: A locational error model for spatial features. Int. J. Geographical Information Science 12(6), 607–620 (1998)

    Article  Google Scholar 

  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. 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. Mamoulis, N., Papadias, D.: Slot index spatial join. IEEE Transaction on Knowledge and Data Engineering 15(1), 211–231 (2003)

    Article  Google Scholar 

  18. Minnesota Planning. Positional accuracy handbook (October 1999), http://www.mnplan.state.mn.us/press/accurate.html

  19. U.S. Bureau of the Census. Census 2000 TIGER/Line Data. Washington DC (2000)

    Google Scholar 

  20. Balakrishnan, N., Rao, C.R.: Order statistics: theory and methods. Elsevier, New York (1998)

    Google Scholar 

  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. 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. 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. 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)

    Chapter  Google Scholar 

  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. Shi, W.: A generic statistical approach for modeling error of geometric features in gis. Int. J. Geographical Information Science 12(2), 131–143 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  30. Zhang, J.X., Goodchild, M.F.: Uncertainty in Geographical Information System. Taylor & Francis, Erewhon (2002)

    Book  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering,  

    Jinfeng Ni & Chinya V. Ravishankar

  2. Department of Electrical Engineering, University of California, Riverside, Riverside, CA, 92521, USA

    Bir Bhanu

Authors
  1. Jinfeng Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chinya V. Ravishankar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bir Bhanu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Research Academic Computer Technology Institute, Patras, Greece

    Thanasis Hadzilacos

  2. Data Engineering Research Lab. Department of Informatics,, Aristotle University, 54124, Thessaloniki, Greece

    Yannis Manolopoulos

  3. Flinders University, Adelaide, Australia

    John Roddick

  4. Department of Informatics, University of Piraeus, Greece

    Yannis Theodoridis

Rights and permissions

Reprints and permissions

Copyright information

© 2003 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ni, J., Ravishankar, C.V., Bhanu, B. (2003). Probabilistic Spatial Database Operations. In: Hadzilacos, T., Manolopoulos, Y., Roddick, J., Theodoridis, Y. (eds) Advances in Spatial and Temporal Databases. SSTD 2003. Lecture Notes in Computer Science, vol 2750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45072-6_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-45072-6_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40535-1

  • Online ISBN: 978-3-540-45072-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation