Knowledge discovery in simulation data — A case study for a backhoe assembly line

Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Schulze, Thomas G.; Akondi, Praneeth; Lemessi, Marco

doi:10.1109/wsc.2017.8248162

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…This process allows us to use the Data Farming concept alongside Data Mining and Visual Analytics (Keim et al 2008) in order to discover complex relations in the model and ultimately gain knowledge. We have shown in several real world case studies (Feldkamp et al 2016;Feldkamp et al 2017) that this approach can indeed uncover hidden relations in the model. While this approach constitutes a hybrid method in the sense that multiple analysis methods are used in combination with simulation, the simulation model itself does not necessarily have to be a hybrid system model (HSM) for this; on the contrary it is most often a traditional model using a monolithic modeling approach.…”

Section: Machine Learning and Simulation (Steffen Strassburger)mentioning

confidence: 90%

Hybrid Simulation Development – Is It Just Analytics?

Bell

Groen

Mustafee

et al. 2019

2019 Winter Simulation Conference (WSC)

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Hybrid simulations can take many forms, often connecting a diverse range of hardware and software components with heterogeneous data sets. The scale of examples is also diverse with both the highperformance computing community using high-performance data analytics (HPDA) to the synthesis of software libraries or packages on a single machine. Hybrid simulation configuration and output analysis is often akin to analytics with a range of dashboards, machine learning, data aggregations and graphical representation. Underpinning the visual elements are hardware, software and data architectures that execute hybrid simulation code. These are wide ranging with few generalized blueprints, methods or patterns of development. This panel will discuss a range of hybrid simulation development approaches and endeavor to uncover possible strategies for supporting the development and coupling of hybrid simulations. 1JONATHAN OZIK is a Computational Scientist at Argonne National Laboratory and Senior Scientist in the Consortium for Advanced Science and Engineering at the University of Chicago. He develops applications of large-scale agent-based models, including models of infectious diseases, healthcare interventions, biological systems, water use and management, critical materials Bell, Groen, Mustafee, Ozik, and Strassburger supply chains, and critical infrastructure. He also applies large-scale model exploration across modeling methods, including ABM, microsimulation and machine/deep learning. Dr. Ozik leads the Repast project (repast.github.io) for agent-based modeling toolkits and the Extreme-scale Model Exploration with Swift (EMEWS) framework for large-scale model exploration capabilities on high performance computing resources (emews.org

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Section: Machine Learning and Simulation (Steffen Strassburger)mentioning

confidence: 90%

Hybrid Simulation Development – Is It Just Analytics?

Bell

Groen

Mustafee

et al. 2019

2019 Winter Simulation Conference (WSC)

View full text Add to dashboard Cite

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“…These large amounts of simulation data can then be analyzed using data mining algorithms. Besides the proof of the general applicability through various case studies [10,11,35], our previous research mainly focused on the computational side regarding suitable data mining methods.…”

Section: Introductionmentioning

confidence: 99%

Visualization and Interaction for Knowledge Discovery in Simulation Data

Feldkamp¹,

Bergmann²,

Straßburger³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Discrete-event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems. Besides traditional simulation studies that focus on single model aspects, data farming describes an approach for using the simulation model as a data generator for broad scale experimentation with a broader coverage of the system behavior. On top of that we developed a process called knowledge discovery in simulation data that enhances the data farming concept by using data mining methods for the data analysis. In order to uncover patterns and causal relationships in the model, a visually guided analysis then enables an exploratory data analysis. While our previous work mainly focused on the application of suitable data mining methods, we address suitable visualization and interaction methods in this paper. We present those in a conceptual framework followed by an exemplary demonstration in an academic case study.

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