Approach to Integration of Data Mining Techniques in Simulation Results Analysis

Irīna Šitova, Jelena Pečerska

Abstract


The research is carried out in the area of analysis of simulation results by using data mining techniques. The goal of the research is to explore the applicability of data mining techniques in the area of simulation results analysis, to offer an application scheme of data mining techniques in the analysis of simulation results, as well as to demonstrate the usage of these techniques in the analysis of experimental data. As a result of the theoretical study, an approach is proposed, consisting of two stages and combining the fundamental techniques of data farming and knowledge discovery. A variety of data mining techniques, such as correlation analysis, clustering and several visualization mechanisms of results, are used for knowledge discovery. The proposed approach is applied to the analysis of experimental data. The performance of a queueing system is analysed, and knowledge and decision rules are obtained from simulation results.


Keywords:

Data mining; discrete-event system simulation; simulation results analysis; queueing system

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References


M. Pidd, Systems Modelling: Theory and Practice. Chichester: John Wiley & Sons Ltd, 2004, 192 p.

A. M. Law, and W. Kelton, Simulation modeling and analysis, 3rd ed. Mc Graw Hill Higher Education, 2000.

J. Banks, and J. S. Carson, B. L. Nelson, & D. Nicol, Discrete-event System Simulation, 5th ed. Upper Saddle River, NJ, USA: Prentice Hall, 2010.

J. Merkurjevs, J. Pečerska, and J. Tolujevs, “Simulation-Based Analysis of Logistic Systems,” Humanities and Social Sciences. Latvia, vol. 4, no. 57, pp. 27–48, 2008.

T. F. Brady, and R. A. Bowden, “The effectiveness of generic optimization routines in computer simulation languages,” Proceedings of the 2001 Industrial Engineering Research Conference, 2001.

T. F. Brady, E. Yelling, “Simulation data mining: A new form of computer simulation output,” Proceedings of the 2005 Winter Simulation Conference, 2005. https://doi.org/10.1109/WSC.2005.1574262

N. Feldkamp, S. Bergmann, S. Strassburger, “Knowledge Discovery in Manufacturing Simulations,” Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, 2015. https://doi.org/10.1145/2769458.2769468

N. Feldkamp, S. Bergmann, S. Strassburger, T. Schulze, “Knowledge discovery in simulation data: A case study of a gold mining facility,” Proceedings of the 2016 Winter Simulation Conference, 2016. https://doi.org/10.1109/WSC.2016.7822210

D. Kibira, Q. Hatim, S. Kumara, et al. “Integrating data analytics and simulation methods to support manufacturing decision making,” Proceedings of the 2015 Winter Simulation Conference, 2015. https://doi.org/10.1109/WSC.2015.7408324

M. K. Painter et al., “Using simulation, data mining, and knowledge discovery techniques for optimized aircraft engine fleet management,” Proceedings of the 2006 Winter Simulation Conference, 2006.

https://doi.org/10.1109/WSC.2006.323221

M. Dunham, Data Mining: Introductory and Advanced Topics. Pearson Education, Inc., 2003, p. 315.

C. G. Cassandras, and S. Lafortune, Introduction to Discrete Event Systems, Second edition., Boston, MA, USA: Springer, 2008. https://doi.org/10.1007/978-0-387-68612-7

J. Han, and M. Kamber, Data Mining: Concepts and Techniques, Second Edition, San Francisco, CA, USA: Elsevier Inc., 2006.

I. H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, San Francisco, CA, USA: Elsevier Inc., 2005.

Y. Zhao, R and Data Mining: Examples and Case Studies, First edition, Elsevier Inc., 2013.

I. Sitova, and J. Pecherska, “A concept of simulation-based SC performance analysis using SCOR metrics,” Information Technology and Management Science. vol. 20, no. 1, pp. 85–90, 2017. https://doi.org/10.1515/itms-2017-0015

S. Robinson, “A tutorial on conceptual modeling for simulation,” Proceedings of the 2015 Winter Simulation Conference, 2015. https://doi.org/10.1109/WSC.2015.7408298




DOI: 10.7250/itms-2018-0014

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