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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)

Abstract

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.

Keywords

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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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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