Skip to main content

Data Reduction

  • Reference work entry
Encyclopedia of Database Systems

Definition

Data reduction means the reduction on certain aspects of data, typically the volume of data. The reduction can also be on other aspects such as the dimensionality of data when the data is multidimensional. Reduction on any aspect of data usually implies reduction on the volume of data.

Data reduction does not make sense by itself unless it is associated with a certain purpose. The purpose in turn dictates the requirements for the corresponding data reduction techniques. A naive purpose for data reduction is to reduce the storage space. This requires a technique to compress the data into a more compact format and also to restore the original data when the data needs to be examined. Nowadays, storage space may not be the primary concern and the needs for data reduction come frequently from database applications. In this case, the purpose for data reduction is to save computational cost or disk access cost in query processing.

Historical Background

The need for data reduction...

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 2,500.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Recommended Reading

  1. http://www.cs.brandeis.edu/∼dcc/index.html.

  2. Ali M.E., Zhang R., Tanin E., and Kulik L. A motion-aware approach to continuous retrieval of 3D objects. In Proc. 24th Int. Conf. on Data Engineering, 2008.

    Google Scholar 

  3. Barbará D., DuMouchel W., Faloutsos C., Haas P.J., Hellerstein J.M., Ioannidis Y.E., Jagadish H.V., Johnson T., Ng R.T., Poosala V., Ross K.A., and Sevcik K.C. The New Jersey data reduction report. IEEE Data Eng. Bull., 20(4):3–45, 1997.

    Google Scholar 

  4. Guha S., Rastogi R., and Shim K. CURE: an efficient clustering algorithm for large databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1998, pp. 73–84.

    Google Scholar 

  5. Jolliffe I.T. Principal component analysis. Springer, Berlin, 1986.

    Google Scholar 

  6. Lelewer D.A. and Hirschberg D.S. Data compression. ACM Comput. Surv., 19(3):261–296, 1987.

    MATH  Google Scholar 

  7. Poosala V., Ioannidis Y.E., Haas P.J., and Shekita E.J. Improved histograms for selectivity estimation of range predicates. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1996, pp. 294–305.

    Google Scholar 

  8. The JPEG 2000 standard. http://www.jpeg.org/jpeg2000/index.html.

  9. Zhang T., Ramakrishnan R., and Livny M. BIRCH: an efficient data clustering method for very large databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1996, pp. 103–114.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Zhang, R. (2009). Data Reduction. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_533

Download citation

Publish with us

Policies and ethics