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Proceedings Paper

Knowledge discovery in scientific data
Author(s): Stephan Rudolph
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Paper Abstract

From many industrial projects large collections of data from experiments and numerical simulations have been collected in the past. Knowledge discovery in scientific data from technical processes, i.e. the extraction of the hidden engineering knowledge in form of a mathematical model description of the experimental data is therefore a major challenge and an important part in the industrial re- engineering information processing chain for an improved future knowledge reuse. Scientific data possess special properties because of their domain of origin. Based on these properties of scientific data, a similarity transformation using the measurement unit information of the data can be performed. This similarity transformation eliminates the scale-dependence of the numerical data values and creates a multitude of dimensionless similarity numbers. Together with several reasoning strategies from artificial intelligence, such as case-based reasoning and neural networks, these similarity numbers may be used to estimate many engineering properties of the technical process under consideration.

Paper Details

Date Published: 6 April 2000
PDF: 9 pages
Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000); doi: 10.1117/12.381739
Show Author Affiliations
Stephan Rudolph, Univ. Stuttgart (Germany)


Published in SPIE Proceedings Vol. 4057:
Data Mining and Knowledge Discovery: Theory, Tools, and Technology II
Belur V. Dasarathy, Editor(s)

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