Visualization and Interaction for Knowledge Discovery in Simulation Data
Visualization and Interaction for Knowledge Discovery in Simulation Data
复制标题
仿真数据中知识发现的可视化和交互
DOI：
10.24251/hicss.2020.165
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
S. Strassburger
中科院分区：
文献类型：
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作者：
Niclas Feldkamp;S. Bergmann;S. Strassburger
离散事件仿真是研究复杂制造和物流系统动态行为的一种成熟而流行的技术。除了关注单一模型方面的传统仿真研究之外，数据农场描述了一种使用仿真模型作为数据生成器的方法，用于更广泛地覆盖系统行为的大规模实验。在此基础上，我们开发了一个称为模拟数据知识发现的过程，通过使用数据挖掘方法进行数据分析，增强了数据农业的概念。为了揭示模型中的模式和因果关系，可视化指导的分析可以进行探索性数据分析。我们以前的工作主要集中在合适的数据挖掘方法的应用上，我们在本文中讨论了合适的可视化和交互方法。我们在一个概念框架中提出这些，然后在一个学术案例研究中进行示范。
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.