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A stable graph layout algorithm for processes

机译:一种稳定的图形布局算法

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摘要

Process mining enables organizations to analyze data about their (business) processes. Visualization is key to gaining insight into these processes and the associated data. Process visualization requires a high-quality graph layout that intuitively represents the semantics of the process. Process analysis additionally requires interactive filtering to explore the process data and process graph. The ideal process visualization therefore provides a high-quality, intuitive layout and preserves the mental map of the user during the visual exploration. The current industry standard used for process visualization does not satisfy either of these requirements. In this paper, we propose a novel layout algorithm for processes based on the Sugiyama framework. Our approach consists of novel ranking and order constraint algorithms and a novel crossing minimization algorithm. These algorithms make use of the process data to compute stable, high-quality layouts. In addition, we use phased animation to further improve mental map preservation. Quantitative and qualitative evaluations show that our approach computes layouts of higher quality and preserves the mental map better than the industry standard. Additionally, our approach is substantially faster, especially for graphs with more than 250 edges.
机译:流程挖掘使组织能够分析有关其(业务)流程的数据。可视化是获得对这些进程和相关数据的洞察的关键。过程可视化需要高质量的图形布局,直观地代表过程的语义。过程分析另外需要交互式过滤来探索过程数据和过程图。因此,理想的过程可视化提供了高质量,直观的布局,并在视觉探索期间保留用户的心理贴图。用于过程可视化的当前行业标准不满足这些要求。在本文中,我们提出了一种基于Sugiyama框架的过程的新颖布局算法。我们的方法包括小说排名和订单约束算法和一种新型交叉最小化算法。这些算法利用过程数据来计算稳定,高质量的布局。此外,我们使用相位的动画进一步改善心理地图保存。定量和定性评估表明,我们的方法计算更高质量的布局,并比行业标准更好地保留心理图。此外,我们的方法显着更快,特别是对于具有超过250个边的图表。

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