Skip to main content

An Efficient Zoning Technique for Multi-dimensional Access Methods

  • Conference paper
Trends in Enterprise Application Architecture (TEAA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3888))

Abstract

In emerging database applications that deal with large sets of multidimensional data, the performance of the query system significantly depends on the performance of its access methods and the underlying disk system. In recent years, hard disks are manufactured with multiple physical zones, where seek times and data transfer rates vary significantly across the zones. However, there is a marked lack of investigation on how to optimize multidimensional access methods given a zoned disk model. The paper proposes a novel dynamic zoning technique called DMD-Zoning that can be applied to a variety of multidimensional access methods and that can fully utilize zoning characteristics of hard disks for busy multi-user database systems.

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

Access this chapter

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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beckman, N., et al.: The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. In: ACM SIGMOD International Conference on Management of Data, pp. 322–331 (1990)

    Google Scholar 

  2. Berchtold, S., Bohm, C., Kriegel, H.-P.: The Pyramid-technique: Towards breaking the curse of dimensionality. In: Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 142–153 (1998)

    Google Scholar 

  3. Berchtold, S., Keim, D., Kriegel, H.-P.: The X-tree: An index structure for highdimensional data. In: Proc. VLDB Int. Conf. on Very Large Data Bases, pp. 28–39 (1996)

    Google Scholar 

  4. Comer, D.: The Ubiquitous B-tree. ACM Computing Surveys 11, 121–137 (1979)

    Article  MATH  Google Scholar 

  5. Faloutsos, C., Kamel, I.: On Packing R-tree. In: Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM), pp. 490–499 (1993)

    Google Scholar 

  6. Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 47–54 (1984)

    Google Scholar 

  7. Leutenegger, S.T., Lopez, M.A.: The Effect of Buffering on the Performance of Rtrees. IEEE Transactions on Knowledge and Data Engineering 12(1), 33–44 (2000)

    Article  Google Scholar 

  8. Leutenegger, S.T., Lopez, M.A., Edingnton, J.M.: STR: A Simple and Efficient Algorithm for R-tree Packing. In: IEEE International Conference on Data Engineering, pp. 497–506 (1997)

    Google Scholar 

  9. Lin, K., Jagadish, H., Faloutsos, C.: The TV-tree: An Index Structure for High- Dimensional Data. VLDB Journal 3, 517–542 (1995)

    Article  Google Scholar 

  10. Ng, S.W.: Advances in Disk Technology: Performance Issues. IEEE Computer Magazine, 75–81 (1998)

    Google Scholar 

  11. Orlandic, R., Yu, B.: A Retrieval Technique for High-Dimensional Data and Partially Specified Queries. DKE Data & Knowledge Engineering 42(2), 1–21 (2002)

    Article  MATH  Google Scholar 

  12. Orlandic, R., Yu, B.: Scalable QSF-Trees: Retrieving Regional Objects in High- Dimensional Spaces. Journal of Database Management 15(3), 45–59 (2004)

    Article  Google Scholar 

  13. Papadias, D., Theodoridis, Y., Sellis, T., Egenhofer, M.J.: Topological relations in the world of minimum bounding rectangles: A study with R-trees. In: Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 92–103 (1995)

    Google Scholar 

  14. Robinson, J.T.: The K-D-B Tree: A Search Structure for Large Multidimensional Dynamic Indexes. In: Proc. ACM SIGMOD Int. Conf. on Management of Data, pp. 10–18 (1981)

    Google Scholar 

  15. Rosenberg, A.L., Snyder, L.: Time- and Space- Optimality in B-trees. ACM Transactions on Database Systems 6(1), 174–193 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  16. Roussopoulos, N., Leifker, D.: Direct Spatial Search on Pictorial Database Using Packed R-trees. In: ACM International Conference on Management of Data, pp. 17–31 (1985)

    Google Scholar 

  17. Ruemmler, C., Wilkes, J.: An Introduction to Disk Drive Modeling. IEEE Computer (March 1994)

    Google Scholar 

  18. White, D.A., Jain, R.: Similarity Indexing with the SS-tree. In: Proc. 12th IEEE Conf. on Data Engineering, pp. 516–523 (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yu, B., Kim, S.H. (2006). An Efficient Zoning Technique for Multi-dimensional Access Methods. In: Draheim, D., Weber, G. (eds) Trends in Enterprise Application Architecture. TEAA 2005. Lecture Notes in Computer Science, vol 3888. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11681885_11

Download citation

  • DOI: https://doi.org/10.1007/11681885_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32734-9

  • Online ISBN: 978-3-540-32735-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics