Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu


  • Torben Bach PedersenEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_885


Numerical fact


A measure is a numerical property of a multidimensional cube, e.g., sales price, coupled with an aggregation formula, e.g., SUM. It captures numerical information to be used for aggregate computations.

Key Points

As an example, a three-dimensional cube for capturing sales may have a Product dimension P, a Time dimension T, and a Store dimension S, capturing the product sold, the time of sale, and the store it was sold in, for each sale, respectively. The cube has two measures: DollarSales and ItemSales, capturing the sales price and the number of items sold, respectively. ItemSales can be viewed as a function: ItemSales: Dom(P) × Dom(T) × Dom(S) ↦ 0 that given a certain combination of dimension values returns the total number of items sold for that combination. If a dimension value corresponds to a higher level in the dimension hierarchy, e.g., a product group or even all products, the result is an aggregation of several lower-level measure values [2]....

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Recommended Reading

  1. 1.
    Gray J, Chaudhuri S, Bosworth A, Layman A, Reichart D, Venkatrao M, Pellow F, Pirahesh H. Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min Knowl Discov. 1997;1(1):29–53.CrossRefGoogle Scholar
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    Jensen CS, Pedersen TB, Thomsen C. Multidimensional databases and data warehousing, Synthesis lectures on data management. San Rafael: Morgan Claypool; 2010.zbMATHCrossRefGoogle Scholar
  3. 3.
    Kimball R, Ross M, Thornthwaite W, Mundy J, Becker B. The data warehouse lifecycle toolkit. 2nd ed. Indianapolis: Wiley; 2008.Google Scholar
  4. 4.
    Pedersen TB, Jensen CS, Dyreson CE. A foundation for capturing and querying complex multidimensional data. Inf Syst. 2001;26(5):383–423.zbMATHCrossRefGoogle Scholar
  5. 5.
    Pedersen TB. Managing complex multidimensional data. In: Aufaure M-A, Zimányi E, editors. Business intelligence – second European summer school, eBISS 2012. Brussels: Springer LNBIB; 2013. July 15–21, 2012, Tutorial Lectures.Google Scholar
  6. 6.
    Vaisman A, Zimányi E. Data warehouse systems – design and implementation. Berlin: Springer; 2014.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark

Section editors and affiliations

  • Torben Bach Pedersen
    • 1
  • Stefano Rizzi
    • 2
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.DISIUniv. of BolognaBolognaItaly