Knowledge Discovery in Manufacturing Simulations

Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen

doi:10.1145/2769458.2769468

Cited by 28 publications

(12 citation statements)

References 9 publications

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“…In order to analyze the generated massive amount of simulation data, some form of automated analysis is needed. To enhance this process, we developed a concept called knowledge discovery in simulation data for using data mining and visualization methods that can be applied on massive scale farmed simulation data, integrating the aspects of data farming, data mining and visual analytics [8].…”

Section: Data Farmingmentioning

confidence: 99%

“…With the widespread availability of Big Data infrastructures and ease of access to computing power, there are new possibilities for leveraging the potential of simulation models. In this regard, we developed a concept called Knowledge Discovery in Simulation Data that allows to use a combination of data mining and visual analysis in order to find hidden and potentially interesting and useful knowledge in the system outside of prior defined project scopes [8]. The process can support the decision-making in a manufacturing context that may lead to outside-of-thebox solutions that the analyst possibly did not think of before.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Data Farmingmentioning

confidence: 99%

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

Self Cite

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“…Feldkamp et al in [7] develop and test the proposed approach by formulating an applicable management strategy in [8]. The sequential application of data mining techniques -correlation analysis, clustering, and best cluster selection -resulted in a finding of prevailing input parameter values.…”

Section: E Further Development Of Data Mining Based Approaches Actualitiesmentioning

confidence: 99%

“…Publications [7] and [8] influenced the creation of the authors' approach. In the previously mentioned publications, a method is described, where data farming and knowledge discovery are combined.…”

Section: E Further Development Of Data Mining Based Approaches Actualitiesmentioning

confidence: 99%

“…In the previously mentioned publications, a method is described, where data farming and knowledge discovery are combined. The authors' approach also consists of these parts, however, unlike works [7] and [8], another combination of the implemented data mining methods is applied. In the developed approach, each of the used technologies has its own purpose:…”

Section: E Further Development Of Data Mining Based Approaches Actualitiesmentioning

confidence: 99%

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Approach to Integration of Data Mining Techniques in Simulation Results Analysis

Šitova

Pečerska

2018

ITMS

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The research is carried out in the area of analysis of simulation results by using data mining techniques. The goal of the research is to explore the applicability of data mining techniques in the area of simulation results analysis, to offer an application scheme of data mining techniques in the analysis of simulation results, as well as to demonstrate the usage of these techniques in the analysis of experimental data. As a result of the theoretical study, an approach is proposed, consisting of two stages and combining the fundamental techniques of data farming and knowledge discovery. A variety of data mining techniques, such as correlation analysis, clustering and several visualization mechanisms of results, are used for knowledge discovery. The proposed approach is applied to the analysis of experimental data. The performance of a queueing system is analysed, and knowledge and decision rules are obtained from simulation results.

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