skip to main content
10.1145/2769458.2769468acmconferencesArticle/Chapter ViewAbstractPublication PagespadsConference Proceedingsconference-collections
research-article

Knowledge Discovery in Manufacturing Simulations

Published: 10 June 2015 Publication History

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.

References

[1]
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.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
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.
[8]
Laney, D. 2001. 3D Data Management: Controlling Data Volume, Velocity, and Variety. In Application Delivery Strategies, Number 949, META Group Inc, Stamford.
[9]
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.
[10]
MongoDB Inc. 2010. Why Schemaless? http://blog.mongodb.org/post/119945109/why-schemaless. Accessed 10 February 2015.
[11]
Sanchez, S. M. 2011. NOLHdesigns spreadsheet. http://harvest.nps.edu/. Accessed 1 February 2015.
[12]
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.
[13]
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.
[14]
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.
[15]
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.
[16]
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.

Cited By

View all
  • (2024)Enhancing Machine Learning for Situation Aware Dispatching Through Generative Adversarial Network Based Synthetic Data Generation2024 Winter Simulation Conference (WSC)10.1109/WSC63780.2024.10838933(1785-1796)Online publication date: 15-Dec-2024
  • (2023)From Explainable AI to Explainable Simulation: Using Machine Learning and XAI to understand System RobustnessProceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3573900.3591114(96-106)Online publication date: 21-Jun-2023
  • (2023)How Not to Visualize Your Simulation Output Data2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10407704(1351-1362)Online publication date: 10-Dec-2023
  • Show More Cited By

Index Terms

  1. Knowledge Discovery in Manufacturing Simulations

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    SIGSIM PADS '15: Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation
    June 2015
    300 pages
    ISBN:9781450335836
    DOI:10.1145/2769458
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 10 June 2015

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. data farming
    2. data mining
    3. knowledge discovery
    4. simulation
    5. visual analytics

    Qualifiers

    • Research-article

    Conference

    SIGSIM-PADS '15
    Sponsor:

    Acceptance Rates

    SIGSIM PADS '15 Paper Acceptance Rate 35 of 60 submissions, 58%;
    Overall Acceptance Rate 398 of 779 submissions, 51%

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)17
    • Downloads (Last 6 weeks)3
    Reflects downloads up to 17 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Enhancing Machine Learning for Situation Aware Dispatching Through Generative Adversarial Network Based Synthetic Data Generation2024 Winter Simulation Conference (WSC)10.1109/WSC63780.2024.10838933(1785-1796)Online publication date: 15-Dec-2024
    • (2023)From Explainable AI to Explainable Simulation: Using Machine Learning and XAI to understand System RobustnessProceedings of the 2023 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation10.1145/3573900.3591114(96-106)Online publication date: 21-Jun-2023
    • (2023)How Not to Visualize Your Simulation Output Data2023 Winter Simulation Conference (WSC)10.1109/WSC60868.2023.10407704(1351-1362)Online publication date: 10-Dec-2023
    • (2022)Explainable AI For Data Farming Output Analysis: A Use Case for Knowledge Generation Through Black-Box Classifiers2022 Winter Simulation Conference (WSC)10.1109/WSC57314.2022.10015304(1152-1163)Online publication date: 11-Dec-2022
    • (2022)Towards an Efficient, Comprehensive Value Stream Planning in Production NetworksProcedia CIRP10.1016/j.procir.2022.05.062107(782-787)Online publication date: 2022
    • (2021)Data farming output analysis using explainable AIProceedings of the Winter Simulation Conference10.5555/3522802.3522879(1-12)Online publication date: 13-Dec-2021
    • (2021)Data Farming Output Analysis Using Explainable AI2021 Winter Simulation Conference (WSC)10.1109/WSC52266.2021.9715470(1-12)Online publication date: 12-Dec-2021
    • (2021)Data Farming in Production Systems - A Review on Potentials, Challenges and Exemplary ApplicationsProcedia CIRP10.1016/j.procir.2021.01.15696(230-235)Online publication date: 2021
    • (2021)Defect Prediction on Production LineAdvances in Computational Intelligence Systems10.1007/978-3-030-87094-2_47(532-544)Online publication date: 18-Nov-2021
    • (2020)Knowledge Discovery in Simulation DataACM Transactions on Modeling and Computer Simulation10.1145/339129930:4(1-25)Online publication date: 25-Nov-2020
    • Show More Cited By

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media