Skip to main content
SpringerLink
Log in

Performance of nearest neighbor queries in R-trees

  • Contributed Papers
  • Conference paper
  • First Online:
Database Theory — ICDT '97 (ICDT 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1186))

Included in the following conference series:

Abstract

Nearest neighbor (NN) queries are posed very frequently in spatial applications. Recently a branch- and-bound algorithm based on R-trees has been developed in order to answer efficiently NN queries. In this paper, we combine techniques that were inherently used for the analysis of range and spatial join queries, in order to derive measures regarding the performance of NN queries. We try to estimate the number of disk accesses introduced due to the processing of an NN query. Lower and upper bounds are defined estimating the performance of NN queries very closely. The theoretical analysis is verified with experimental results, under uniform and non-uniform distributions of queries and data, in the 2-dimensional address space.

Work supported by European Union's TMR program and by national PENED and EPET programs.

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. W. Aref: “Query Processing and Optimization in Spatial Databases”, Technical Report CS-TR-3097, Department of Computer Science, University of Maryland at College Park, MD, 1993.

    Google Scholar 

  2. M. Arya, W. Cody, C. Faloutsos, J. Richardson and A. Toga: “QBISM: a Prototype 3-d Medical Image Database System”, IEEE Data Engineering Bulletin, 16(1), pp.38–42, March 1993.

    Google Scholar 

  3. N. Beckmann, H.P. Kriegel and B. Seeger: “The R*-tree: an Efficient and Robust Method for Points and Rectangles”, Proceedings of the 1990 ACM SIGMOD Conference, pp.322–331, Atlantic City, NJ, 1990.

    Google Scholar 

  4. A. Belussi and C. Faloutsos: “Estimating the Selectivity of Spatial Queries Using the ‘Correlation’ Fractal Dimension”, Proceedings of the 21th VLDB Conference, pp.299–310, Zurich, Switzerland, 1995.

    Google Scholar 

  5. T. Brinkhoff, H.P. Kriegel and B. Seeger: “Efficient Processing of Spatial Join Using R-trees”, Proceedings of the 1990 ACM SIGMOD Conference, pp.237–246, Washington DC, 1993.

    Google Scholar 

  6. M. Egenhofer: “Spatial SQL: a Query and Presentation Language”, IEEE Transactions on Knowledge and Data Engineering, vol.6, no.1, pp.86–95, 1994.

    Google Scholar 

  7. R. Fagin: “Combining Fuzzy Information from Multiple Systems”, Proceedings of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS '96), pp.216–226, Montreal, Canada, 1996.

    Google Scholar 

  8. C. Faloutsos and I. Kamel: “Beyond Uniformity and Independence, Analysis of R-trees Using the Concept of Fractal Dimension”, Proceedings of the 13th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS '94), pp.4–13, Minneapolis, MN, 1994.

    Google Scholar 

  9. J.H. Friedman, J.L. Bentley and R.A. Finkel: “An Algorithm for Finding the Best Matches in Logarithmic Expected Time”, ACM Transactions on Math. Software, vol.3, pp.209–226, 1977.

    Google Scholar 

  10. O. Guenther: “The Design of the Cell Tree: an Object-Oriented Index Structure for Geometric Databases”, Proceedings of the 5th IEEE Conference on Data Engineering, pp.598–615, Los Angeles, CA, 1989.

    Google Scholar 

  11. R.H. Guting: “An Introduction to Spatial Database Systems”, The VLDB Journal, vol.3, no.4, pp.357–399, 1994.

    Google Scholar 

  12. A. Guttman: “R-trees: a Dynamic Index Structure for Spatial Searching”, Proceedings of the 1984 ACM SIGMOD Conference, pp.47–57, Boston, MA, 1984.

    Google Scholar 

  13. A. Henrich, H.W. Six and P. Widmayer: “The LSD-tree: Spatial Access to Multidimensional Point and non-Point Objects”, Proceedings of the 15th VLDB Conference, pp.45–53, Amsterdam, Netherlands, 1989.

    Google Scholar 

  14. I. Kamel and C. Faloutsos: “On Packing R-trees”, Proceedings of the 2nd Conference on Information and Knowledge Management (CIKM), Washington DC 1993.

    Google Scholar 

  15. I. Kamel and C. Faloutsos: “Hilbert R-tree: an Improved R-tree Using Fractals”, Proceedings of the 20th VLDB Conference, pp.500–509, Santiago, Chile, 1994.

    Google Scholar 

  16. R. Laurini and D. Thompson: “Fundamentals of Spatial Information Systems”, Academic Press, London, 1992.

    Google Scholar 

  17. M.L. Lo and C.V. Ravishankar: “Spatial Joins Using Seeded Trees”, Proceedings of the 1994 ACM SIGMOD Conference, pp.209–220, Minneapolis MN 1994.

    Google Scholar 

  18. W. Niblack, R. Barber, W. Equitz, M. Flickner, E. Glasman, D. Petkovic and P. Yanker: “The QBIC Project: Querying Images by Content Using Color, Texture and Shape”, Proceedings of the SPIE Conference on Storage and Retrieval for Image and Video Databases, vol.1908, pp.173–187, 1993.

    Google Scholar 

  19. J. Orenstein: “Spatial Query Processing in an Object-Oriented Database System”, Proceedings of the 1986 ACM SIGMOD Conference, pp.326–336, Washington DC, 1986.

    Google Scholar 

  20. B.U. Pagel, H.W. Six, H. Toben and P. Widmayer: “Towards an Analysis of Range Query Performance in Spatial Data Structures”, Proceedings of the 12th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS '93), pp.214–221, Washington DC, 1993.

    Google Scholar 

  21. A. Papadopoulos and Y. Manolopoulos: “Multiple Range Query Optimization in Spatial Databases”, Information Systems, submitted.

    Google Scholar 

  22. N. Roussopoulos and D. Leifker: “Direct Spatial Search on Pictorial Databases Using Packed R-trees”, Proceedings of the 1985 ACM SIGMOD Conference, pp.17–31, Austin, TX, 1985.

    Google Scholar 

  23. N. Roussopoulos, S. Kelley and P. Vincent: “Nearest Neighbor Queries”, Proceedings of the 1995 ACM SIGMOD Conference, pp.71–79, San Jose, CA, 1995.

    Google Scholar 

  24. H. Samet: “The Design and Analysis of Spatial Data Structures”, Addison-Wesley, MA, 1990.

    Google Scholar 

  25. H. Samet: “Applications of Spatial Data Structures: Computer Graphics, Image Processing and GIS”, Addison-Wesley, MA, 1990.

    Google Scholar 

  26. T. Sellis, N. Roussopoulos and C. Faloutsos: “The R+-tree: a Dynamic Index for Multidimensional Objects”, Proceedings of the 13th VLDB Conference, pp.507–518, Brighton, UK, 1987.

    Google Scholar 

  27. Y. Theodoridis and T. Sellis: “A Model for the Prediction of R-tree Performance”, Proceedings of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS '96), Montreal, Canada, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Aristotle University, 54006, Thessaloniki, Greece

    Apostolos Papadopoulos & Yannis Manolopoulos

Authors
  1. Apostolos Papadopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yannis Manolopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Foto Afrati Phokion Kolaitis

Rights and permissions

Reprints and permissions

Copyright information

© 1996 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Papadopoulos, A., Manolopoulos, Y. (1996). Performance of nearest neighbor queries in R-trees. In: Afrati, F., Kolaitis, P. (eds) Database Theory — ICDT '97. ICDT 1997. Lecture Notes in Computer Science, vol 1186. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-62222-5_59

Download citation

  • DOI: https://doi.org/10.1007/3-540-62222-5_59

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-62222-2

  • Online ISBN: 978-3-540-49682-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation