A Hybrid M&S Methodology for Knowledge Discovery

  • Jae Kwon Kim
  • Jong Sik Lee
  • Kang Sun LeeEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10228)


M&S (Modeling and Simulation) has been widely used as a decision supporting tool by modeling the structure and dynamics of real-world systems on a computer and simulating the models to answer various what-if questions. As simulation models become complex in their dynamics and structures, more engineers are experiencing difficulties to simulate the models with various real-world scenarios and to discover knowledge from the massive amount of simulation results within a practical time bound. In this paper, we propose a hybrid methodology where the M&S process is combined with a DM (Data Mining) process. Our methodology includes a step to inject simulation outputs to a DM process which generates a prediction model by analyzing pertaining patterns in the simulation outputs. The prediction model can be used to replace simulations, if we need to expedite the M&S-based decision making process. We have applied the proposed methodology to analyze SAM (Surface-to-air missile) and confirmed the applicability.


Modeling and simulation Data mining Knowledge discovery Simulation output analysis 



This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract UD080042AD, Republic of Korea.


  1. 1.
    Taylor, S.J., Khan, A., Tolk, K.L., Morse, A., Yilmaz, L., Zander, J.: Grand challenges on the theory of modeling and simulation. In: Proceedings of the Symposium on Theory of Modeling & Simulation-DEVS Integrative M&S, p. 34 (2013)Google Scholar
  2. 2.
    Jiawei, H., Kamber, M.: Data Mining: Concepts and Techniques. The Morgan Kaufmann Series, 2nd edn., pp. 1–6. Elsevier, Amsterdam (2006)Google Scholar
  3. 3.
    Zeigler, B.P., Praehofer, H., Kim, T.G.: Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. Academic Press, Cambridge (2000). pp. 76–96Google Scholar
  4. 4.
    Remondino, M., Correndo, G.: Data mining applied to agent based simulation. In: Proceedings of the 19th European Conference on Modelling and Simulation, Riga, Latvia (2005)Google Scholar
  5. 5.
    Painter, M.K., Erraguntla, M., Hogg Jr., G.L., Beachkofski, B.: Using simulation, data mining, and knowledge discovery techniques for optimized aircraft engine fleet management. In: Proceedings of the 38th Conference on Winter Simulation, pp. 1253–1260 (2006)Google Scholar
  6. 6.
    Trépos, R., Masson, V., Cordier, M.O., Gascuel-Odoux, C., Salmon-Monviola, J.: Mining simulation data by rule induction to determine critical source areas of stream water pollution by herbicides. Comput. Electron. Agric. 86, 75–88 (2012)CrossRefGoogle Scholar
  7. 7.
    Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: Neural Networks, IJCNN International Joint Conference, pp. 593–605 (1989)Google Scholar
  8. 8.
    Filippone, A.: Advanced Aircraft Flight Performance. Cambridge University Press, New York (2012)CrossRefGoogle Scholar
  9. 9.
    Leeman, E.L.: Tactical Missile Design, 2nd edn. American Institute of Aeronautics and Astronautics, Reston (2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer and Information EngineeringInha UniversityIncheonSouth Korea
  2. 2.Department of Computer EngineeringMyongji UniversityYonginSouth Korea

Personalised recommendations