Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Spatial Databases

SSD 1999: Advances in Spatial Databases pp 167–187Cite as

Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1651))

Abstract

The polygon amalgamation operation computes the boundary of the union of a set of polygons. This is an important operation for spatial on-line analytical processing and spatial data mining, where polygons representing different spatial objects often need to be amalgamated by varying criteria when the user wants to aggregate or reclassify these objects. The processing cost of this operation can be very high for a large number of polygons. Based on the observation that not all polygons to be amalgamated contribute to the boundary, we investigate in this paper efficient polygon amalgamation methods by excluding those internal polygons without retrieving them from the database. Two novel algorithms, adjacency-based and occupancy-based, are proposed. While both algorithms can reduce the amalgamation cost significantly, the occupancy-based algorithm is particularly attractive because: 1) it retrieves a smaller amount of data than the adjacency-based algorithm; 2) it is based on a simple extension to a commonly used spatial indexing mechanism; and 3) it can handle fuzzy amalgamation.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. D.J. Abel. SIRO-DBMS: A database toolkit for geographical information systems. Int. J. Geographical Information Systems, 4(3):443–464, 1989.

    Google Scholar 

  2. D.J. Abel and J.L. Smith. A data structure and algorithm based on a linear key for a rectangle retrieval problem. Computer Vision, Graphics and Image Processing, 24(1):1–13, 1983.

    Article  Google Scholar 

  3. T. Brinkho, H.P. Kriegel, and B. Seeger. Efficient processing of spatial joins using R-trees. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 237–246, Washington, D. C., 1993.

    Google Scholar 

  4. S. Chaudhuri and U. Dayal. An overview of data warehousing and olap technology. SIGMOD Record, 26(1), 1997.

    Google Scholar 

  5. D.J. DeWitt and R.H. Gerber. Multiprocessor hash-based join algorithms. In Proc. 1985 Int. Conf. on Very Large Data Bases, pages 151–164, Austin, Texas, 1985.

    Google Scholar 

  6. M.J. Egenhofer. Reasoning about binary topological relationships. In LNCS 552: Proceedings of 2nd Int. Symp. on Spatial Databases (SSD’91), pages 143–160. Springer-Verlag, 1991.

    Google Scholar 

  7. M. Ester, H.-P. Kriegel, and J. Sander. Spatial data mining: A database approach. In LNCS 1262: Proceedings of the 5th Int. Symp. on Spatial Databases (SSD’97), pages 47–66, Berlin, Germany, 1997. Springer-Verlag.

    Google Scholar 

  8. M. Ester, H.-P. Kriegel, J. Sander, and X. XU. Density-connected sets and their application for trend detection in spatial databases. In Proc. 3rd Int. Conf. Knowledge Discovery and Data Mining (KDD’97), pages 10–15, Newport Beach, California, 1997.

    Google Scholar 

  9. V. Gaede. Optimal redundancy in spatial database systems. In M. J. Egenhofer and J. R. Herring, editors, LNCS 951: Proc. 4th Int. Symp. on Spatial Databases (SSD’95), pages 96–116, Portland, Maine, 1995. Springer-Verlag.

    Google Scholar 

  10. V. Gaede and O. Günther. Multidimensional access methods. ACM Computing Surveys, 30(2):170–231, 1998.

    Article  Google Scholar 

  11. O. Günther. Efficient computation of spatial joins. In Proceedings of 9th International Conference on Data Engineering, pages 50–59, Vienna, Austria, 1993.

    Google Scholar 

  12. R.H. Güting. An introduction to spatial database systems. /LDB Journal, 3(4):357–399, 1994.

    Google Scholar 

  13. A. Guttman. R-trees: A dynamic index structure for spatial searching. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 47–54, Boston, Massachusetts, 1984.

    Google Scholar 

  14. J. Han, K. Koperski, and N. Stefanovic. GeoMiner: A system prototype for spatial data mining. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 560–563, Tucson, Arizona, 1997.

    Google Scholar 

  15. J. Han, N. Stefanovic, and K. Koperski. Selective materialization: An efficient method for spatial data cube construction. In Proc. Pacific-Asia Conf. on Knowledge Discovery and Data Mining, pages 144–158, Melbourne, Australia, 1998.

    Google Scholar 

  16. V. Harinarayan, A. Rajaraman, and J.D. Ullman. Implementing data cubes efficiently. In Proc. 1996 ACM SIGMOD Int. Conf. on Management of Data, pages 206–216, Montreal, Canada, 1996.

    Google Scholar 

  17. W. Lu, J. Han, and B.C. Ooi. Knowledge discovery in large spatial databases. In Proc. Far East Workshop Geographic Information Systems, pages 275–289, Singapore, 1993.

    Google Scholar 

  18. R. Ng and J. Han. Efficient and efective clustering method for spatial data mining. In Proceedings of 1994 International Conference on Very Large Data Bases, pages 144–155, Santiago, Chile, 1994.

    Google Scholar 

  19. J. Orenstein. Redundancy in spatial databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 294–305, Portland, Oregon, 1989.

    Google Scholar 

  20. J. Orenstein and F.A. Manola. Probe spatial data modeling and query processing in an image database application. IEEE Trans. on Software Eng., 14(5):611–629, 1988.

    Article  Google Scholar 

  21. J.M. Patel and D.J. DeWitt. Partition based spatial-merge join. In Proc. ACM SIGMOD Int. Conf. on Management of Data, pages 259–270, Montreal, Canada, 1996.

    Google Scholar 

  22. F.P. Preparata and M.I. Shamos. Computational Geometry: an introduction. Springer-Verlag, 1985.

    Google Scholar 

  23. H. Samet. Applications of Spatial Data Structures. Addison-Wesley, 1990.

    Google Scholar 

  24. T. Sellis, N. Roussopoulos, and C. Faloutsos. The R+-tree: a dynamic index for multi-dimensional objects. In Proc. 13th Int. Conf. on Very Large Data Bases, pages 3–11, Brighton, England, 1987.

    Google Scholar 

  25. D. Truffet and M.E. Orlowska. Two phase query processing with fuzzy approximations. In Proc. 4th ACM International Workshop on Advances in Geographic Information Systems (ACM-GIS’96), Rockville, USA, 1996.

    Google Scholar 

  26. X. Zhou, D.J. Abel, and D. Truffet. Data partitioning for parallel spatial join processing. In LNCS 1262: Proc. 5th Int. Symp. on Spatial Databases (SSD’97), pages 178–196, Berlin, Germany, 1997. Springer-Verlag.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Electrical Engineering, University of Queensland, Brisbane, QLD 4072, Australia

    Xiaofang Zhou

  2. CSIRO Mathematical and Information Sciences, PO Box 664, Canberra, ACT 2601, Australia

    David Truffet

  3. School of Computing Sciences, Simon Fraser University, Burnany, B. C., Canada, V5A 1S6

    Jiawei Han

Authors
  1. Xiaofang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. David Truffet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiawei Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Praktische Informatik IV, Fernuniversität Hagen, D-58084, Hagen, Germany

    Ralf Hartmut Güting

  2. Department of Computer Science, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China

    Dimitris Papadias  & Fred Lochovsky  & 

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Zhou, X., Truffet, D., Han, J. (1999). Efficient Polygon Amalgamation Methods for Spatial OLAP and Spatial Data Mining. In: Güting, R.H., Papadias, D., Lochovsky, F. (eds) Advances in Spatial Databases. SSD 1999. Lecture Notes in Computer Science, vol 1651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48482-5_12

Download citation

  • DOI: https://doi.org/10.1007/3-540-48482-5_12

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66247-1

  • Online ISBN: 978-3-540-48482-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation