Visualization and Interaction for Knowledge Discovery in Simulation Data Feldkamp, Niclas Bergmann, Sören Strassburger, Steffen 2020-01-04T07:25:24Z 2020-01-04T07:25:24Z 2020-01-07
dc.description.abstract 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.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2020.165
dc.identifier.isbn 978-0-9981331-3-3
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Interactive Visual Analytics and Visualization for Decision Making – Making Sense of a Growing Digital World
dc.subject data farming
dc.subject data mining
dc.subject manufacturing systems
dc.subject simulation
dc.title Visualization and Interaction for Knowledge Discovery in Simulation Data
dc.type Conference Paper
dc.type.dcmi Text
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