Gillmann Christina, Maack Robin Georg Claus, Raith Felix, Perez Juan F, Scheuermann Gerik
IEEE Comput Graph Appl. 2023 Sep-Oct;43(5):62-71. doi: 10.1109/MCG.2023.3299297. Epub 2023 Sep 14.
Visual analytics (VA) has become a standard tool to process and analyze data visually to generate novel insights. Unfortunately, each component can introduce uncertainty in the visual analytics process. These uncertainty events can originate from many effects and need to be differentiated. In this work, we propose a taxonomy of potential uncertainty events in the visual analytics cycle. Here, we structure the taxonomy along the components included in the visual analytics cycle. Based on this taxonomy, we provide a list of dependencies between these events. At last, we show how to use our taxonomy by providing a real-world example.
可视化分析（VA）已成为一种通过可视化方式处理和分析数据以产生新颖见解的标准工具。不幸的是，每个组件都可能在可视化分析过程中引入不确定性。这些不确定性事件可能源于多种影响，需要加以区分。在这项工作中，我们提出了可视化分析周期中潜在不确定性事件的分类法。在此，我们根据可视化分析周期中包含的组件来构建分类法。基于此分类法，我们提供了这些事件之间的依赖关系列表。最后，我们通过一个实际示例展示如何使用我们的分类法。
