Automatic Detection of Structural Changes in Data Warehouses

  • Johann Eder
  • Christian Koncilia
  • Dieter Mitsche
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)


Data Warehouses provide sophisticated tools for analyzing complex data online, in particular by aggregating data along dimensions spanned by master data. Changes to these master data is a frequent threat to the correctness of OLAP results, in particular for multi- period data analysis, trend calculations, etc. As dimension data might change in underlying data sources without notifying the data warehouse, we are exploring the application of data mining techniques for detecting such changes and contribute to avoiding incorrect results of OLAP queries.


Singular Value Decomposition Discrete Cosine Transform Discrete Fourier Transform Data Mining Technique Master Data 
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.


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  1. [Atk89]
    Atkinson, K.E.: An Introduction to Numerical Analysis. John Wiley, New York (1989)zbMATHGoogle Scholar
  2. [BD02]
    Brockwell, P.J., Davis, R.A.: Introduction to Time Series Forecasting. Springer, New York (2002)zbMATHCrossRefGoogle Scholar
  3. [BSH99]
    Blaschka, M., Sapia, C., Höfling, G.: On Schema Evolution in Multidimensional Databases. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 153–164. Springer, Heidelberg (1999)Google Scholar
  4. [CS99]
    Chamoni, P., Stock, S.: Temporal Structures in Data Warehousing. In: Mohania, M., Tjoa, A.M. (eds.) DaWaK 1999. LNCS, vol. 1676, pp. 353–358. Springer, Heidelberg (1999)Google Scholar
  5. [Dia00]
    Diacu, F.: An Introduction to Differential Equations - Order and Chaos. W. H. Freeman, New York (2000)Google Scholar
  6. [EK01]
    Eder, J., Koncilia, C.: Changes of dimension data in temporal data warehouses. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2001. LNCS, vol. 2114, p. 284. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  7. [EKM02]
    Eder, J., Koncilia, C., Morzy, T.: The COMET Metamodel for Temporal Data Warehouses. In: Pidduck, A.B., Mylopoulos, J., Woo, C.C., Ozsu, M.T. (eds.) CAiSE 2002. LNCS, vol. 2348, p. 83. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. [Hol02]
    Hollmen, J.: Principal component analysis (2002),
  9. [JD98]
    Jensen, C.S., Dyreson, C.E. (eds.): A consensus Glossary of Temporal Database Concepts, pp. 367–405. Springer, Heidelberg (1998); In: [EJS98]Google Scholar
  10. [Vai01]
    Vaisman, A.: Updates, View Maintenance and Time Management in Multidimensional Databases. Universidad de Buenos Aires, Ph.D. Thesis (2001)Google Scholar
  11. [Vid99]
    Vidakovic, B.: Statistical Modeling by Wavelets. John Wiley, New York (1999)zbMATHCrossRefGoogle Scholar
  12. [Wei85]
    Weisberg, S.: Applied Linear Regression. John Wiley, New York (1985)zbMATHGoogle Scholar
  13. [Yan01]
    Yang, J.: Temporal Data Warehousing. Stanford University, Ph.D. Thesis (June 2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Johann Eder
    • 1
  • Christian Koncilia
    • 1
  • Dieter Mitsche
    • 1
  1. 1.Dep. of Informatics-SystemsUniversity of Klagenfurt 

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