Advertisement

Optimal Partitioning for Efficient I/O in Spatial Databases

  • Hakan Ferhatosmanoglu
  • Divyakant Agrawal
  • Amr El Abbadi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2150)

Abstract

It is desirable to design partitioning techniques that minimize the I/O time incurred during query execution in spatial databases. In this paper, we explore optimal partitioning techniques for spatial data for different types of queries, and develop multi-disk allocation techniques that maximize the degree of I/O parallelism obtained during the retrieval. We show that hexagonal partitioning has optimal I/O cost for circular queries compared to all possible non-overlapping partitioning techniques that use convex regions. For rectangular queries, we show that although for the special case when queries are rectilinear, rectangular grid partitioning gives superior performance, hexagonal partitioning has overall better I/O cost for a general class of range queries. We then discuss parallel storage and retrieval techniques for hexagonal partitioning using current techniques for rectangular grid partitioning.

Keywords

Data Space Range Query Spatial Database Query Point Optimal Partitioning 
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.
    H. C. Du and J. S. Sobolewski. Disk allocation for cartesian product files on multiple-disk systems. ACM Transactions of Database Systems, 7(1):82–101, March 1982.Google Scholar
  2. 2.
    H. Ferhatosmanoglu, D. Agrawal, and A. El Abbadi. Concentric hyperspaces and disk allocation for fast parallel range searching. In Proc. Int. Conf. Data Engineering, pages 608–615, Sydney, Australia, March 1999.Google Scholar
  3. 3.
    H. Ferhatosmanoglu, D. Agrawal, and A. El Abbadi. Optimal partitioning for spatial data. Technical report, Comp. Sci. Dept., UC, Santa Barbara, December 2000.Google Scholar
  4. 4.
    V. Gaede and O. Gunther. Multidimensional access methods. ACM Computing Surveys, 30:170–231, 1998.CrossRefGoogle Scholar
  5. 5.
    Thomas C. Hales. The honeycomb conjecture. available at http://xxx.lanl.gov/abs/math.MG/9906042, June 1999.
  6. 6.
    Thomas C. Hales. Historical background on hexagonal honeycomb. http://www.math.lsa.umich.edu/hales/countdown/honey/hexagonHistory.html, March 2000.
  7. 7.
    S. Prabhakar, K. Abdel-Ghaffar, D. Agrawal, and A. El Abbadi. Cyclic allocation of two-dimensional data. In International Conference on Data Engineering, pages 94–101, Orlando, Florida, Feb 1998.Google Scholar
  8. 8.
    H. Samet. The Design and Analysis of Spatial Structures. Addison Wesley Publishing Company, Inc., Massachusetts, 1989.Google Scholar
  9. 9.
    R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In Proceedings of the Int. Conf. on Very Large Data Bases, pages 194–205, New York City, New York, August 1998.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Hakan Ferhatosmanoglu
    • 1
  • Divyakant Agrawal
    • 1
  • Amr El Abbadi
    • 1
  1. 1.Computer Science DepartmentUniversity of California at Santa BarbaraUSA

Personalised recommendations