Knowledge Discovery from Production Databases for Hierarchical Process Control
The paper gives the results of the project that was oriented on the usage of knowledge discoveries from production systems for needs of the hierarchical process control. One of the main project goals was the proposal of knowledge discovery model for process control. Specifics data mining methods and techniques was used for defined problems of the process control. The gained knowledge was used on the real production system thus the proposed solution has been verified. The paper documents how is possible to apply the new discovery knowledge to use in the real hierarchical process control. There are specified the opportunities for application of the proposed knowledge discovery model for hierarchical process control.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1088950Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1300
 U. M. Fayyad, "Data Mining and Knowledge Discovery: Making Sense Out of Data”. IEEE Expert/Intelligent Systems & Their Applications, pp. 20–26, 1996.
 U. M. Fayyad, G. Piatetski–Shapiro, G. P. Smyth, "From Data Mining to Knowledge Discovery: An Overview”. Advances in Knowledge Discovery and Data Mining, MIT Press, pp. 1–37, 1996.
 R. Halenar, "Matlab Routines Used for Real Time ETL Method”. Applied Mechanics and Materials, pp. 2125-2129, 2012.
 R. Halenar, "Real Time ETL Improvement”. International Journal of Computer Theory and Engineering. vol. 4, no. 3, pp. 405-409, 2012.
 J. Jadlovsky, "Proposal of distribution control system of FMS” in International Conference Cybernetics and Informatics, Vysna Boca 2010.
 P. Mydlo, T. Skulavik, P. Schreiber, "The fuzzy PI controller`s chosen parametres influence on the regulation process” in Process Control 2010, University of Pardubice, pp. C055a1-8, 2010.
 M. A. Schwarz, Introduction to software engineering for secure and reliable software – Einführung in die Softwaretechnik für sichere und verlässliche Software. Institut für Informatik im Paderbom, 2004.
 A. Trnka, "Classification and Regression Trees as a Part of Data Mining in Six Sigma Methodology” in World Congress on Engineering and Computer Science, Hong Kong: International Association of Engineers, pp. 449-453, 2010.
 A.-W. Sheer, CIM Computer Integrated Manufacturing. Berlin: Springer-Verlag, 2011.