Data Mining in Scientific Data

  • Stephan Rudolph
  • Peter Hertkorn
Part of the Massive Computing book series (MACO, volume 3)


Knowledge discovery in scientific data, i.e. the extraction of engineering knowledge in form of a mathematical model description from experimental data, is currently an important part in the industrial re-engineering effort for an improved knowledge reuse. Despite the fact that large collections of data have been acquired in expensive investigations from numerical simulations and experiments in the past, the systematic use of data mining algorithms for the purpose of knowledge extraction from data is still in its infancy.

In contrary to other data sets collected in business and finance, scientific data possess additional properties special to their domain of origin. First, the principle of cause and effect has a strong impact and implies the completeness of the parameter list of the unknown functional model more rigorous than one would assume in other domains, such as in financial credit-worthiness data or client behavior analyses. Secondly, scientific data are usually rich in physical unit information which represents an important piece of structural knowledge in the underlying model formation theory in form of dimensionally homogeneous functions.

Based on these features of scientific data, a similarity transformation using the measurement unit information of the data can be performed. This similarity transformation eliminates the scale-dependency of the numerical data values and creates a set of dimensionless similarity numbers. Together with reasoning strategies from artificial intelligence such as case-based reasoning, these similarity number may be used to estimate many engineering properties of the technical object or process under consideration. Furthermore, the employed similarity transformation usually reduces the remaining complexity of the resulting unknown similarity function which can be approximated using different techniques.


Data Mining Scientific Data Similarity Transformation Data Mining Algorithm Dimensionless Group 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Aamodt, A., and E. Plaza, “Case-based reasoning: Foundational issues, methodological variations, and system approaches”, in AI Communications, 7(1): 39–59, 1994.Google Scholar
  2. Bluman, G. W., and S. Kumei, Symmetries and Differential Equations. New York: Springer, 1989.CrossRefzbMATHGoogle Scholar
  3. Bronstein, I. N., and K. A. Semendjajew, Taschenbuch der Mathematik, 19 edition, Thun: Ham Deutsch, 1981.zbMATHGoogle Scholar
  4. Buckingham, E., “On physically similar systems: Illustration of the use of dimensional equations”, Physical Review, 4: 345–376, 1914.CrossRefGoogle Scholar
  5. Chatterjee, N., and J. A. Campbell, “Interpolation as a means of fast adaptation in case-based problem solving”, in Proceedings Fifth German Workshop on Case-Based Reasoning, pp. 65–74, 1997.Google Scholar
  6. Fayyad, U. M., D. Hausler, and P. Stolorz, “Mining scientific data”, Communications of the ACM, 39 (11): 51–57, 1996.CrossRefGoogle Scholar
  7. Fayyad, U. M., G. Piatetsky-Shapiro, and P. Smyth, “From data mining to knowledge discovery: An overview”, in Advances in Knowledge Discovery and Data Mining, pp. 1–34, Menlo Park: AAAUMIT Press, 1996.Google Scholar
  8. Görtler, H., Dimensionsanalyse. Theorie der physikalischen Dimensionen mit Anwendungen. Berlin: Springer, 1975.zbMATHGoogle Scholar
  9. Hertkorn, P., and S. Rudolph, “Dimensional analysis in case-based reasoning”, in Proceedings International Workshop on Similarity Methods, pp. 163–178, Stuttgart: Insitut für Statik und Dynamik der Luft-und Raumfahrtkonstruktionen, 1998.Google Scholar
  10. Hertkorn, P., and S. Rudolph, “Exploiting similarity theory for case-based reasoning in real-valued engineering design problems”, in Proceedings Artificial Intelligence in Design ‘88, pp. 345–362, Dordrecht: Kluwer, 1998.Google Scholar
  11. Hertkorn, P., and S. Rudolph, “A systematic method to identify patterns in engineering data”, in Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, pp. 273280, 2000.Google Scholar
  12. Holman, J., Heat Transfer. New York: McGraw-Hill, 1986.Google Scholar
  13. Kolodner, J. L, Case-Based Reasoning. San Mateo: Morgan Kaufmann, 1993.CrossRefzbMATHGoogle Scholar
  14. Liu, H., and R. Setiono, “Dimensionality reduction via discretization”, Knowledge Based Systems, 9 (1): 71–77, 1996.CrossRefGoogle Scholar
  15. Maher, M. L, M. B. Balachandran, and D. M. Zhang, Case-Based Reasoning in Design. Mahwah: Lawrence Erlbaum,, 1995.Google Scholar
  16. Rudolph, S., “Eine Methodik zur systematischen Bewertung von Konstruktionen”, Düsseldorf: VDI-Verlag, 1995.Google Scholar
  17. Shapiro, S., Encyclopedia of Artificial Intelligence. New York, Wiley, 1987.Google Scholar
  18. Slade, S., “Case-based reasoning”, AI Magazine, 91 (1): 42–55, 1991.Google Scholar
  19. Szirtes, T., Applied dimensional analysis and modeling. New York: Mc Graw-Hill, 1998.zbMATHGoogle Scholar
  20. Till, M., and S. Rudolph, “A discussion of similarity concepts for acoustics based upon dimensional analysis”, in Proceedings 2nd International Workshop on Similarity Methods, pp. 181–195, 1999.Google Scholar
  21. Weß, S., Fallbasiertes Problemlösen in wissensbasierten Systemen zur Entscheidungsunterstützung und Diagnostik. Grundlagen, Systeme und Anwendungen. Kaiserslautern: Universität Kaiserslautern, 1995.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • Stephan Rudolph
    • 1
  • Peter Hertkorn
    • 1
  1. 1.Institute for Statics and Dynamics of Aerospace StructuresUniversity of StuttgartStuttgartGermany

Personalised recommendations