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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)

Abstract

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.

Keywords

Modeling and simulation Data mining Knowledge discovery Simulation output analysis 

Notes

Acknowledgement

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

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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

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