Skip to main content
SpringerLink
Log in

A Unified Approach for Indexed and Non-indexed Spatial Joins

  • Conference paper
  • First Online:
Advances in Database Technology — EDBT 2000 (EDBT 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1777))

Included in the following conference series:

Abstract

Most spatial join algorithms either assume the existence of a spatial index structure that is traversed during the join process, or solve the problem by sorting, partitioning, or on-the-fly index construction. In this paper, we develop a simple plane-sweeping algorithm that unifies the index-based and non-index based approaches. This algorithm processes indexed as well as non-indexed inputs, extends naturally to multi-way joins, and can be built easily from a few standard operations. We present the results of a comparative study of the new algorithm with several index-based and non-index based spatial join algorithms. We consider a number of factors, including the relative performance of CPU and disk, the quality of the spatial indexes, and the sizes of the input relations. An important conclusion from our work is that using an index-based approach whenever indexes are available does not always lead to the best execution time, and hence we propose the use of a simple cost model to decide when to follow an index-based approach.

Supported in part by National Science Foundation grants EIA-9870734 and EIA-9972879.

Supported in part by the U.S. Army Research Office under grant DAAH04-96-1-0013 and by the National Science Foundation under grants CCR-9522047 and EIA-9870734.

This work was done while a Visiting Scholar at Duke University.

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. S. Acharya, V. Poosala, and S. Ramaswamy. Selectivity estimation in spatial databases. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 13–24, 1999.

    Google Scholar 

  2. L. Arge. The buffer tree: A new technique for optimal I/O-algorithms. In Proc. Workshop on Algorithms and Data Structures, LNCS 955, pages 334–345, 1995. A complete version appears as BRICS technical report RS-96-28, University of Aarhus.

    Google Scholar 

  3. L. Arge, R. Barve, O. Procopiuc, L. Toma, D. E. Vengroff, and R. Wickeremesinghe. TPIE User Manual and Reference (edition 0.9.01a). Duke University, 1999. The manual and software distribution are available on the web at http://www.cs.duke.edu/TPIE/.

  4. L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Scalable sweeping-based spatial join. In Proc. Intl. Conf. on Very Large Databases, pages 570–581, 1998.

    Google Scholar 

  5. L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J. S. Vitter. Theory and practice of I/O-efficient algorithms for multidimensional batched searching problems. In ACM-SIAM Symp. on Discrete Algorithms, pages 685–694, 1998.

    Google Scholar 

  6. L. Becker, K. Hinrichs, and U. Finke. A new algorithm for computing joins with grid files. In Proc. IEEE Intl. Conf. on Data Engineering, pages 190–197, 1993.

    Google Scholar 

  7. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. The R*-tree: An efficient and robust access method for points and rectangles. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 322–331, 1990.

    Google Scholar 

  8. T. Brinkhoff, H.-P. Kriegel, and B. Seeger. Efficient processing of spatial joins using R-trees. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 237–246, 1993.

    Google Scholar 

  9. G. S. Brodal and J. Katajainen. Worst-case efficient external-memory priority queues. In Proc. Scandinavian Workshop on Algorithms Theory, LNCS 1432, pages 107–118, 1998.

    Google Scholar 

  10. D. J. DeWitt, N. Kabra, J. M. Patel, and J.-B. Yu. Client-server Paradise. In Proc. Intl. Conf. on Very Large Databases, pages 558–569, 1994.

    Google Scholar 

  11. M. Goodrich, J.-J. Tsay, D. E. Vengroff, and J. S. Vitter. External-memory computational geometry. In Proc. IEEE Symp. on Foundations of Computer Science, pages 714–723, 1993.

    Google Scholar 

  12. O. Günther. Efficient computation of spatial joins. In Proc. IEEE Intl. Conf. on Data Engineering, pages 50–59, 1993.

    Google Scholar 

  13. R. H. Güting and W. Schilling. A practical divide-and conquer algorithm for the rectangle intersection problem. Information Sciences, 42: 95–112, 1987.

    Article  MATH  MathSciNet  Google Scholar 

  14. A. Guttman. R-trees: A dynamic index structure for spatial searching. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 47–57, 1984.

    Google Scholar 

  15. E. G. Hoel and H. Samet. A qualitative comparison study of data structures for large linear segment databases. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 205–214, 1992.

    Google Scholar 

  16. Y.-W. Huang, N. Jing, and E. Rundensteiner. Spatial joins using R-trees: Breadth-first traversal with global optimizations. In Proc. Intl. Conf. on Very Large Databases, pages 396–405, 1997.

    Google Scholar 

  17. I. Kamel and C. Faloutsos. On packing R-trees. In Proc. Intl. Conf. on Information and Knowledge Management, pages 47–57, 1993.

    Google Scholar 

  18. K. Kim and S. K. Cha. Sibling clustering of tree-based spatial indexes for efficient spatial query processing. In Proc. ACM Intl. Conf. Information and Knowledge Management, pages 398–405, 1998.

    Google Scholar 

  19. M. Kitsuregawa, L. Harada, and M. Takagi. Join strategies on KD-tree indexed relations. In Proc. IEEE Intl. Conf. on Data Engineering, pages 85–93, 1989.

    Google Scholar 

  20. N. Koudas and K. C. Sevcik. Size separation spatial join. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 324–335, 1996.

    Google Scholar 

  21. M.-L. Lo and C. V. Ravishankar. Spatial joins using seeded trees. In Proc. SIG-MOD Intl. Conf. on Management of Data, pages 209–220, 1994.

    Google Scholar 

  22. M.-L. Lo and C. V. Ravishankar. Generating seeded trees from data sets. In Proc. Intl. Symp. on Spatial Databases, LNCS 951, pages 328–347, 1995.

    Google Scholar 

  23. M.-L. Lo and C. V. Ravishankar. Spatial hash-joins. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 247–258, 1996.

    Google Scholar 

  24. N. Mamoulis and D. Papadias. Integration of spatial join algorithms for joining multiple inputs. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 1–12, 1999.

    Google Scholar 

  25. N. Mamoulis, D. Papadias, and Y. Theodoridis. Processing and optimization of multiway spatial joins using R-trees. In Proc. Symp. on Principles of Database Systems, pages 44–55, 1999.

    Google Scholar 

  26. D. R. Musser and A. Saini. STL Tutorial and Reference Guide: C++ Programming with the Standard Template Library. Addison-Wesley, 1996.

    Google Scholar 

  27. J. Nievergelt, H. Hinterberger, and K. C. Sevcik. The grid file: An adaptable, symmetric multikey file structure. ACM Trans. on Database Systems, 9(1):38–71, March 1984.

    Article  Google Scholar 

  28. J. A. Orenstein. A comparison of spatial query processing techniques for native and parameter spaces. In Proc. SIGMOD Intl. Conf. on Management of Data, pages 343–352, 1990.

    Google Scholar 

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

    Article  Google Scholar 

  30. J. M. Patel and D. J. DeWitt. Partition based spatial-merge join. In Proc. SIG-MOD Intl. Conf. on Management of Data, pages 259–270, 1996.

    Google Scholar 

  31. F. P. Preparata and M. I. Shamos. Computational Geometry: An Introduction. Springer, Berlin, 2nd edition, 1988.

    Google Scholar 

  32. D. Rotem. Spatial join indices. In Proc. IEEE Intl. Conf. on Data Engineering, pages 500–509, 1991.

    Google Scholar 

  33. H. Samet. The Design and Analysis of Spatial Data Structures. Addison-Wesley, 1990.

    Google Scholar 

  34. T. Sellis, N. Roussopoulos, and C. Faloutsos. The R+-tree: A dynamic index for multi-dimensional objects. In Proc. Intl. Conf. on Very Large Databases, pages 507–518, 1987.

    Google Scholar 

  35. TIGER/Line™Files, 1997 Technical Documentation. Washington, DC, September 1998. http://www.census.gov/geo/tiger/TIGER97D.pdf.

  36. U. Vahalia. UNIX Internals: The New Frontiers. Prentice Hall, 1996.

    Google Scholar 

  37. P. Valduriez. Join indices. ACM Trans. on Database Systems, 12(2):218–246, June 1987.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Center for Geometric Computing, Department of Computer Science, Duke University, Durham, NC, 27708-0129

    Lars Arge, Octavian Procopiuc & Jeffrey Scott Vitter

  2. Epiphany, 2300 Geng Road, Palo Alto, CA

    Sridhar Ramaswamy

  3. Computer and Information Science, Polytechnic University, 6 MetroTech Center, Brooklyn, NY, 11201

    Torsten Suel

  4. Institut für Informatik, Westfälische Wilhelms-Universität, 48149, Münster, Germany

    Jan Vahrenhold

Authors
  1. Lars Arge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Octavian Procopiuc
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sridhar Ramaswamy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Torsten Suel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jan Vahrenhold
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jeffrey Scott Vitter
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Computer Science Department, University of California, Los Angeles, CA, 90095, USA

    Carlo Zaniolo

  2. Computer Science Department, University of Karlsruhe, P.O. Box 6980, 76128, Karlsruhe, Germany

    Peter C. Lockemann

  3. University of Konstanz, P.O. Box D188, 78457, Konstanz, Germany

    Marc H. Scholl  & Torsten Grust  & 

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Arge, L., Procopiuc, O., Ramaswamy, S., Suel, T., Vahrenhold, J., Vitter, J.S. (2000). A Unified Approach for Indexed and Non-indexed Spatial Joins. In: Zaniolo, C., Lockemann, P.C., Scholl, M.H., Grust, T. (eds) Advances in Database Technology — EDBT 2000. EDBT 2000. Lecture Notes in Computer Science, vol 1777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46439-5_29

Download citation

  • DOI: https://doi.org/10.1007/3-540-46439-5_29

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67227-2

  • Online ISBN: 978-3-540-46439-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation