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Encyclopedia of Database Systems pp 684–692Cite as

Data Warehousing Systems: Foundations and Architectures

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Definition

A data warehouse (DW) is an integrated repository of data for supporting decision-making applications of an enterprise. The most widely cited definition of a DW is from Inmon [3] who states that “a data warehouse is a subject-oriented, integrated, nonvolatile, and time-variant collection of data in support of management’s decisions.”

Historical Background

DW systems have evolved from the needs of decision-making based on integrated data, rather than an individual data source. DW systems address the two primary needs of enterprises: data integration and decision support environments. During the 1980s, relational database technologies became popular. Many organizations built their mission-critical database systems using the relational database technologies. This trend proliferated many independent relational database systems in an enterprise. For example, different business lines in an enterprise built separate database systems at different geographical locations. These...

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Recommended Reading

  1. Adamson C. and Venerable M. Data Warehouse Design Solutions. Wiley, New York, 1998.

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  2. Cunningham C., Song I.-Y., and Chen P.P. 2006.Data warehouse design for customer relationship management. J. Database Manage., 17(2):62–84,

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  3. Inmon W.H. Building the Data Warehouse, 3rd edn., Wiley, New York, 2002.

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  4. Jones M.E. and Song I.-Y. 2008.Dimensional modeling: identification, classification, and evaluation of patterns. Decis. Support Syst., 45(1):59–76,

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  5. Kimball R. and Merz R. The Data Webhouse Toolkit: Building the Web-Enabled Data Warehouse. Wiley, New York, 2000.

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  6. Kimball R. and Ross M. The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edn., Wiley, 2002.

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  7. Kimball R., Ross M., Thorntwaite W., Munday J., and Becker B. 1The Data Warehouse Lifecycle Toolkit, 2nd  edn., Wiley, 2008.

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  8. Malinowski E. and Zimanyi E. Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications. Springer, 2008.

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  9. Poole J., Chang D., Tolbert D., and Mellor D. Common Warehouse Metamodel: An Introduction to the Standard for Data Warehouse Integration. Wiley, 2002.

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  10. Sen A. and Sinha P. 2005.A comparison of data warehousing methodologies. Commun. ACM, 48(3):79–84,

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  11. Todman C. Designing a Data Warehouse Supporting Customer Relationship Management. Prentice-Hall, 2000.

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  12. Watson H.J. and Ariyachandra T. Data Warehouse Architectures: Factors in the Selection, Decision, and the Success of the Architectures. Technical Report, University of Georgia, 2005. Available from http://www.terry.uga.edu/∼hwatson/DW_Architecture_Report.pdf

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Author information

Authors and Affiliations

  1. Drexel University, Philadelphia, PA, USA

    Il-Yeol Song

Authors
  1. Il-Yeol Song
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Editor information

Editors and Affiliations

  1. College of Computing, Georgia Institute of Technology, 266 Ferst Drive, 30332-0765, Atlanta, GA, USA

    LING LIU (Professor) (Professor)

  2. Database Research Group David R. Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, N2L 3G1, Waterloo, ON, Canada

    M. TAMER ÖZSU (Professor and Director, University Research Chair) (Professor and Director, University Research Chair)

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© 2009 Springer Science+Business Media, LLC

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Song, IY. (2009). Data Warehousing Systems: Foundations and Architectures. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_121

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