Integrating Different Grain Levels in a Medical Data Warehouse Federation

  • Marko Banek
  • A Min Tjoa
  • Nevena Stolba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4081)


Healthcare organizations practicing evidence-based medicine strive to unite their data resources in order to achieve a wider knowledge base for sophisticated research and matured decision support service. The central point of such an integrated system is a data warehouse, to which all participants have access. In order to insure a better protection of highly sensitive healthcare data, the warehouse is not created physically, but as a federated system. The paper describes the conceptual design of a health insurance data warehouse federation (HEWAF) aimed at supporting evidence-based medicine. We address a major domain-specific conceptual design issue: the integration of low-grained, time-segmented data into the traditional warehouse, whose basic grain level is higher than that of the time-segmented data. The conceptual model is based on a widely accepted international healthcare standard. We use ontologies of the data warehouse domain, as well as of the healthcare and pharmacy domains, to provide schema matching between the federation and the component warehouses.


Data Warehouse Lexical Database Clinical Document Architecture OLAP Query Clinical Data Warehouse 


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  1. 1.
    Bruckner, R.M., Tok, W.L., Mangisengi, O., Tjoa, A.M.: A Framework for a Multidimensional OLAP Model Using Topic Maps. In: Proc. Int. Conf. on Web Information Systems Engineering (WISE 2001), pp. 109–118. IEEE Computer Society, Los Alamitos (2001)Google Scholar
  2. 2.
    Curé, O.: Ontology Interaction with a Patient Electronic Health Record. In: Proc. Symp. Computer-Based Medical Systems (CBMS 2005), pp. 323–328. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  3. 3.
    Ewen, E.F., Medsker, C., Dusterhoft, L.E., Levan-Shultz, K., Smith, J.L., Gottschall, M.A.: Data Warehousing in an Integrated Health System: Building the Business Case. In: Proc. Int. Workshop on Data Warehousing and OLAP (DOLAP 1998), pp. 47–53. ACM Press, New York (1998)CrossRefGoogle Scholar
  4. 4.
    Jindal, R., Acharya, A.: Federated Data warehouse Architecture, Wipro Technologies white paper (2004) (last access April 10, 2006),
  5. 5.
    Kimball, R., Ross, M.: The Data Warehouse Toolkit, The Complete Guide to Dimensional Modeling, 2nd edn. John Wiley & Sons, New York (2002)Google Scholar
  6. 6.
    Laxminarayan, P., Ruiz, C., Alvarez, S.A., Moonis, M.: Mining Associations over Human Sleep Time Series. In: Proc. Symp. Computer-Based Medical Systems (CBMS 2005), pp. 323–328. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar
  7. 7.
    Morocho, V., Saltor, F., Pérez-Vidal, L.: Schema Integration on Federated Spatial DB across Ontologies. In: Proc. Int. Workshop on Engineering Federated Information Systems (EFIS 2003), pp. 63–72. IOS Press, Amsterdam (2003)Google Scholar
  8. 8.
    Nguyen, T.B., Tjoa, A.M., Mangisengi, O.: MetaCube XTM: A Multidimensional Metadata Approach for Semantic Web Warehousing Systems. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 76–88. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Pedersen, T.B., Jensen, C.S.: Research Issues in Clinical Data Warehousing. In: Proc. Int. Conf. on Scientific and Statistical Database Management (SSDBM 1998), pp. 43–52. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  10. 10.
    Song, I.-Y., Rowen, W., Medsker, C., Ewen, E.F.: An Analysis of Many-to-Many Relationships between the Fact and Dimension Tables in Dimensional Modeling. In: Proc. Int. Workshop on Design Management of Data Warehouses (DMDW 2001). CEUR Workshop Proceedings, vol. 39, 6(1-13) (2001), CEUR WS-orgGoogle Scholar
  11. 11.
    Priebe, T., Pernul, G.: Ontology-based Integration of OLAP and Information Retrieval. In: Int. Workshop on Database and Expert Systems Applications (DEXA 2003), pp. 610–614. IEEE Computer Society, Los Alamitos (2003)CrossRefGoogle Scholar
  12. 12.
    Pedersen, D., Riis, K., Pedersen, T.B.: XML Extended OLAP Querying. In: Proc. Int. Conf. on Scientific and Statistical Database Management (SSDBM 2002), pp. 195–206. IEEE Computer Society, Los Alamitos (2002)CrossRefGoogle Scholar
  13. 13.
    Pedersen, D., Riis, K., Pedersen, T.B.: A Powerful and SQL-Compatible Data Model and Query Language for OLAP. In: Database Technologies, Proc. Australasian Database Conference (ADC 2002), Australian Computer Society (2002)Google Scholar
  14. 14.
    Sackett, D.L., Rosenberg, W.M.C., Gray, J.A.M., Haynes, R.B., Richardson, W.S.: Evidence-Based Medicine: What It Is and What It Isn’t. British Medical J. 312(7032), 71–72 (1996)Google Scholar
  15. 15.
    Sheth, A.P., Larson, J.A.: Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases. ACM Computing Surveys 22(3), 183–236 (1990)CrossRefGoogle Scholar
  16. 16.
    World Health Organization (WHO): International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Version for 2003 (last access April 10, 2006),
  17. 17.
    World Wide Web Consortium: XML Path Language (XPath) v. 1.0. W3C recommendation (November 16, 1999),
  18. 18.
    World Wide Web Consortium: OWL Web Ontology Language. W3C Recommendation (February 10, 2004),
  19. 19.
    XML Topic Maps (XTM) 1.0, TopicMaps.Org Specification (June 6, 2001),
  20. 20.
    Princeton University Cognitive Science Laboratory: WordNet, a lexical database for English language (last access April 10, 2006),
  21. 21.
    Health Level Seven (HL7) (last access April 10, 2006),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marko Banek
    • 1
  • A Min Tjoa
    • 2
  • Nevena Stolba
    • 3
  1. 1.FERUniversity of ZagrebZagrebCroatia
  2. 2.Institute of Software Technology and Interactive Systems (ISIS)Vienna University of TechnologyWienAustria
  3. 3.Women’s Postgraduate College for Internet Technologies (WIT)Vienna University of TechnologyWienAustria

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