The Data Mining usage in Production System Management
The paper gives the pilot results of the project that is
oriented on the use of data mining techniques and knowledge
discoveries from production systems through them. They have been
used in the management of these systems. The simulation models of
manufacturing systems have been developed to obtain the necessary
data about production. The authors have developed the way of
storing data obtained from the simulation models in the data
warehouse. Data mining model has been created by using specific
methods and selected techniques for defined problems of production
system management. The new knowledge has been applied to
production management system. Gained knowledge has been tested
on simulation models of the production system. An important benefit
of the project has been proposal of the new methodology. This
methodology is focused on data mining from the databases that store
operational data about the production process.
[1] P. Berka, Knowledge discovering from databases. Academia, 2003.
[2] 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.
[3] P. Giudici, S. Figini, Applied Data Mining for Business and Industry.
2nd Edition. Wiley Computer Publishing, 2009.
[4] M. Kantardzic, J. Zurada, Next Generation of Data-Mining Applications.
Wiley-IEEE Press, 2005.
[5] L. Lacko, Oracle - Administration, programming and using database
system. Computer Press, 2007.
[6] L. Lacko, Database: data warehouse, OLAP and data mining. Computer
Press, 2003.
[7] P. Schreiber, "Applications of Genetic Algorithms". 19th Central
European Conference on Information and Intelligent Systems - CECIIS:
Conference Proceedings. University of Zagreb, 2008.
[8] D. Taniar, Data Mining and Knowledge Discovery Technologies. IGI
Publishing, 2008.
[9] A. Trnka, "Classification and Regression Trees as a Part of Data Mining
in Six Sigma Methodology". WCECS 2010: World Congress on
Engineering and Computer Science, International Association of
Engineers, 2010. pp. 449-453.
[10] C. Vercellis, Business Intelligence: Data Mining and Optimization for
Decision Making. Wiley Computer Publishing, 2009.
[11] G. J. Williams, S. J. Simoff, Data Mining - Theory, Methodology,
Techniques, and Applications. Springer, 2006.
[1] P. Berka, Knowledge discovering from databases. Academia, 2003.
[2] 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.
[3] P. Giudici, S. Figini, Applied Data Mining for Business and Industry.
2nd Edition. Wiley Computer Publishing, 2009.
[4] M. Kantardzic, J. Zurada, Next Generation of Data-Mining Applications.
Wiley-IEEE Press, 2005.
[5] L. Lacko, Oracle - Administration, programming and using database
system. Computer Press, 2007.
[6] L. Lacko, Database: data warehouse, OLAP and data mining. Computer
Press, 2003.
[7] P. Schreiber, "Applications of Genetic Algorithms". 19th Central
European Conference on Information and Intelligent Systems - CECIIS:
Conference Proceedings. University of Zagreb, 2008.
[8] D. Taniar, Data Mining and Knowledge Discovery Technologies. IGI
Publishing, 2008.
[9] A. Trnka, "Classification and Regression Trees as a Part of Data Mining
in Six Sigma Methodology". WCECS 2010: World Congress on
Engineering and Computer Science, International Association of
Engineers, 2010. pp. 449-453.
[10] C. Vercellis, Business Intelligence: Data Mining and Optimization for
Decision Making. Wiley Computer Publishing, 2009.
[11] G. J. Williams, S. J. Simoff, Data Mining - Theory, Methodology,
Techniques, and Applications. Springer, 2006.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:56502", author = "Pavel Vazan and Pavol Tanuska and Michal Kebisek", title = "The Data Mining usage in Production System Management", abstract = "The paper gives the pilot results of the project that is
oriented on the use of data mining techniques and knowledge
discoveries from production systems through them. They have been
used in the management of these systems. The simulation models of
manufacturing systems have been developed to obtain the necessary
data about production. The authors have developed the way of
storing data obtained from the simulation models in the data
warehouse. Data mining model has been created by using specific
methods and selected techniques for defined problems of production
system management. The new knowledge has been applied to
production management system. Gained knowledge has been tested
on simulation models of the production system. An important benefit
of the project has been proposal of the new methodology. This
methodology is focused on data mining from the databases that store
operational data about the production process.", keywords = "data mining, data warehousing, management of
production system, simulation", volume = "5", number = "5", pages = "898-5", }