Niclas Feldkamp
Soeren Bergmann
Steffen Strassburger
ABSTRACT
Discrete event simulation studies in a manufacturing context are a powerful instrument when modeling and evaluating processes of various industries. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually varying parameters through educated assumptions and according to a prior defined goal. Moreover, simulation experts try to reduce complexity and number of simulation runs by excluding parameters that they consider as not influential regarding the simulation project scope. On the other hand, today's world of big data technology enables us to handle huge amounts of data. We therefore investigate the potential benefits of designing large scale experiments with a much broader coverage of possible system behavior. In this paper, we propose an approach for applying data mining methods on simulation data in combination with suitable visualization methods in order to uncover relationships in model behavior to discover knowledge that otherwise would have remained hidden. For a prototypical demonstration we used a clustering algorithm to divide large amounts of simulation output datasets into groups of similar performance values and depict those groups through visualizations to conduct a visual investigation process of the simulation data.
- Böhm, C., Kailing, K., Kröger, P., and Zimek, A. 2004. Computing Clusters of Correlation Connected Objects. In Proceeding of the 2004 ACM SIGMOD international conference (Paris, France, June 13-18, 2004). SIGMOD/PODS '04. ACM, New York, NY, 455--466. DOI = http://doi.acm.org/10.1145/1007568.1007620. Google Scholar
Digital Library
- Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. 1996. From Data Mining to Knowledge Discovery in Databases. AI Magazine 17, 3 (Fall 1996), 37--54.Google ScholarDigital Library
- Han, J. and Kamber, M. 2006. Data mining. Concepts and techniques. The Morgan Kaufmann series in data management systems. Elsevier; Morgan Kaufmann, Amsterdam, Boston, San Francisco, CA. Google ScholarDigital Library
- Hernandez, A. S., Lucas, T. W., and Carlyle, M. 2012. Constructing nearly orthogonal latin hypercubes for any nonsaturated run-variable combination. ACM Trans. Model. Comput. Simul. 22, 4 (Nov. 2012), 1--17. Google ScholarDigital Library
- Horne, G. E. and Meyer, T. E. 2005. Data Farming: Discovering Surprise. In Proceedings of the 2005 Winter Simulation Conference (Orlando, USA, December 04-07, 2005), 1082--1087. DOI = http://dx.doi.org/10.1109/WSC.2005.1574362. Google ScholarDigital Library
- Keim, D. A., Mansmann, F., Schneidewind, J., Thomas, J., and Ziegler, H. 2008. Visual Analytics: Scope and Challenges. In Visual Data Mining: Theory, Techniques and Tools for Visual Analytics, S. Simoff, M. H. Boehlen and A. Mazeika, Eds. Springer, Berlin, Heidelberg. Google ScholarDigital Library
- Kleijnen, J. P. C., Sanchez, S. M., Lucas, T. W., and Cioppa, T. M. 2005. State-of-the-Art Review: A User's Guide to the Brave New World of Designing Simulation Experiments. INFORMS Journal on Computing 17, 3 (Summer 2005), 263--289. Google ScholarDigital Library
- Laney, D. 2001. 3D Data Management: Controlling Data Volume, Velocity, and Variety. In Application Delivery Strategies, Number 949, META Group Inc, Stamford.Google Scholar
- Law, A. M. 2003. How to conduct a successful simulation study. In Proceedings of the 2003 Winter Simulation Conference (New Orleans, USA, December 07-10, 2003), 66--70. DOI= http://dx.doi.org/10.1109/WSC.2003.1261409. Google ScholarDigital Library
- MongoDB Inc. 2010. Why Schemaless? http://blog.mongodb.org/post/119945109/why-schemaless. Accessed 10 February 2015.Google Scholar
- Sanchez, S. M. 2011. NOLHdesigns spreadsheet. http://harvest.nps.edu/. Accessed 1 February 2015.Google Scholar
- Sanchez, S. M. 2014. Simulation Experiments: Better Data, not just Big Data. In Proceedings of the 2014 Winter Simulation Conference, (Savannah, USA, December 07-10, 2014), 805--816. DOI = http://dx.doi.org/10.1109/WSC.2014.7019942. Google ScholarDigital Library
- Thomas, J. J. and Cook, K. A. 2005. Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society, Los Alamitos, CA, USA.Google Scholar
- Wilkinson, L., Anand, A., and Grossman, R. 2006. High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions. IEEE Trans. Visual. Comput. Graphics 12, 6 (Nov./Dec. 2006), 1363--1372. Google ScholarDigital Library
- Frawley, W. J., Piatetsky-Shapiro, G., and Matheus, C. J. 1992. Knowledge Discovery in Databases: An Overview. AI Magazine 13, 3 (Fall 1992), 57--70. Google ScholarDigital Library
- Ye, K. Q. 1998. Orthogonal Column Latin Hypercubes and Their Application in Computer Experiments. Journal of the American Statistical Association 93, 444 (Dec 1998), 1430--1439.Google ScholarCross Ref
Index Terms
- Knowledge Discovery in Manufacturing Simulations
Recommendations
Knowledge Discovery in Simulation Data
Special Issue on Toward an Ecosystem of Models and DataThis article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and ...
Online Analysis of Simulation Data with Stream-based Data Mining
SIGSIM-PADS '17: Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete SimulationDiscrete event simulation is an accepted instrument for investigating the dynamic behavior of complex systems and evaluating processes. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually ...
Surveying the complementary role of automatic data analysis and visualization in knowledge discovery
VAKD '09: Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive ExplorationThe aim of this work is to survey and reflect on the various ways to integrate visualization and data mining techniques toward a mixed-initiative knowledge discovery taking the best of human and machine capabilities. Following a bottom-up bibliographic ...
Comments