Advertisement

Knowledge Integration from Multidimensional Data Sources

  • Wilfried Grossmann
  • Markus Moschner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4739)

Abstract

Information integration plays a substantial role for data warehouses with their needs for highly dynamical adaptivity. For data and knowledge integration an OLAP–based method is discussed with special emphasis on summary level integration. The basic OLAP–model is enlarged by a so called composite structure for documentation of the concepts behind the data according to the needs of statistical information processing. The model supports also bookkeeping of the evolution of the data model.

Keywords

Data Warehouse Knowledge Integration Book Order Semantic Coherence Ontology Integration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Buneman, P.: How to Cite Curated Databases and How to make them Citable. In: Froeschl, K.A., Grossmann, W. (eds.) Proc. 18th Int. Conf. on Scientific and Statistical Database Management– SSDBM 2006, pp. 195–203. IEEE, Los Alamitos (2006)CrossRefGoogle Scholar
  2. 2.
    DDI Data Documentation Initiative— A Project of the Social Science Community–Codebook, http://www.icpsr.umich.edu/DDI/codebook/index.html
  3. 3.
    Denk, M., Froeschl, K.A., Grossmann, W.: Statistical Composites: A Transformation Bound Representation of Statistical Datasets. In: Kennedy, J. (ed.) Proc. 14th Int. Conf. on Scientific and Statistical Database Management, pp. 217–226. IEEE, Los Alamitos (2002)CrossRefGoogle Scholar
  4. 4.
    Goguen, J.A.: Data, Schema and Ontology Integration (Extended Abstract). In: Carnielli, A.W., Dionísio, F.M., Mateus, P.C. (eds.) Proc. of the Workshop on Combination of Logics CombLog 2004, CLE e–prints, vol. 4(5) (2004), http://www.cle.unicamp.br/e_Prints/vol_4,n_5,2004.html
  5. 5.
    Grossmann, W., Moschner, M.: Towards an Ontology for Data in Business Decisions. In: Karagiannis, D., Reimer, U. (eds.) PAKM 2004. LNCS (LNAI), vol. 3336, pp. 397–407. Springer, Heidelberg (2004)Google Scholar
  6. 6.
    Melnik, S.: Generic Model Management. LNCS, vol. 2967. Springer, Heidelberg (2004)zbMATHGoogle Scholar
  7. 7.
    The MetaNet Project, http://www.epros.ed.ac.uk/metanet
  8. 8.
    Pourabbas, E., Shoshani, A.: The Composite OLAP–Object Data Model. Technical report, Lawrence Berkeley National Laboratory (2005), http://www-library.lbl.gov/docs/LBNL/592/29/PDF/LBNL-59229.pdf
  9. 9.
    Pourabbas, E., Shoshani, A.: The Composite OLAP–Object Data Model: Removing an Unnecessary Barrier. In: Froeschl, K.A., Grossmann, W. (eds.) Proc. 18th Int. Conf. on Scientific and Statistical Database Management– SSDBM 2006, pp. 291–300. IEEE, Los Alamitos (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Wilfried Grossmann
    • 1
  • Markus Moschner
    • 2
  1. 1.WG Data Analysis and Computing, Dept. Computer Science, University of Vienna, A–1010 ViennaAustria
  2. 2.WG Applications of Formal Logics, Dept. Computer Languages, TU Vienna, A–1040 ViennaAustria

Personalised recommendations