Metadata for Data Warehousing

  • Prakash M. Nadkarni
Part of the Health Informatics book series (HI)


A data warehouse is kind of database whose architecture (and underlying supporting technology) has been optimized for highly efficient query, at the cost of sacrificing features that support robust interactive inserts, updates and delete actions. The difference between a data warehouse and a data mart (which is also optimized for the same purpose) is partly one of scope. While warehouses are supposed to encompass data across an entire organization, data marts are typically smaller scale (e.g., departmental in scope) though in an ideal situation they would receive data from a warehouse, effectively serving as front-ends to the latter.


Data Warehouse Dimension Table Source System Fact Table Lineage Information 


  1.  1.
    Kimball R, Caserta J. The Data Warehouse ETL Toolkit. New York: Wiley; 2008.Google Scholar
  2.  2.
    Kimball R. The Data Warehousing Toolkit. New York: Wiley; 1997.Google Scholar
  3.  3.
    Inmon W, Rudin K, Buss C, Sousa R. Data Warehouse Performance. New York: Wiley; 1998.Google Scholar
  4.  4.
    Vetterli T, Vaduva A, Staudt M. Metadata standards for data warehousing: open information model vs. common warehouse metamodel. ACM SIGMOD Rec. 2000;29(3):68-75.CrossRefGoogle Scholar
  5.  5.
    Object Management Group. Common warehouse metamodel. 2010 [cited 10/3/10]; Available from:
  6.  6.
    Kimball Group. Design tips. 2010 [cited 11/2/10]; Available from:
  7.  7.
    Mundy J, Thornthwaite W, Kimball R. The Microsoft ® Data Warehouse Toolkit: With SQL Server™ 2005 and the Microsoft ® Business Intelligence Toolset. New York: Wiley; 2006.Google Scholar
  8.  8.
    Leuf B, Cunningham W. The Wiki Way. Quick collaboration on the Web. Reading: Addison-Wesley; 2001.Google Scholar
  9.  9.
    English L. Improving Data Warehouse and Business Information Quality: Methods for reducing costs and increasing profits. New York: Wiley; 1999.Google Scholar
  10. 10.
    Murphy SN, Morgan MM, Barnett GO, Chueh HC. Optimizing healthcare research data warehouse design through past COSTAR query analysis. Proc AMIA Symp. 1999:892-896.Google Scholar
  11. 11.
    Murphy SN, Barnett GO, Chueh HC. Visual query tool for finding patient cohorts from a clinical data warehouse of the partners HealthCare system. Proc AMIA Symp. 2000:1174.Google Scholar
  12. 12.
    Murphy SN, Chueh HC. A security architecture for query tools used to access large biomedical databases. Proc AMIA Symp. 2002:552-556.Google Scholar
  13. 13.
    Murphy SN, Gainer V, Chueh HC. A visual interface designed for novice users to find research patient cohorts in a large biomedical database. AMIA Annu Symp Proc. 2003:489-493.Google Scholar
  14. 14.
    Partners Healthcare. I2B2 Data Mart Design document. 2010 [cited 10/1/10]; Available from:

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Prakash M. Nadkarni
    • 1
  1. 1.School of MedicineYale UniversityNew HavenUSA

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