Satyanarayan Arvind, Lee Bongshin, Ren Donghao, Heer Jeffrey, Stasko John, Thompson John, Brehmer Matthew, Liu Zhicheng
IEEE Trans Vis Comput Graph. 2020 Jan;26(1):461-471. doi: 10.1109/TVCG.2019.2934281. Epub 2019 Aug 20.
An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed -Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that would have been daunting within the constituent papers of these three systems. We compare and contrast their (previously unmentioned) limitations and trade-offs between expressivity and learnability. We also reflect on common assumptions that we made during the development of our systems, thereby informing future research directions in visualization authoring systems.
新一代可视化创作系统支持无需文本编程的富有表现力的信息可视化。由于它们在可视化模型、系统架构和用户界面方面存在差异，使用传统评估方法直接比较这些系统具有挑战性。认识到在更广阔的设计空间中为我们的决策提供背景信息的价值，我们对我们开发的三个系统——Lyra、数据插画师和图表制作器——进行了批判性反思。本文揭示了在这三个系统的组成论文中本会令人望而生畏的知识。我们比较并对比了它们（之前未提及的）局限性以及表现力和可学习性之间的权衡。我们还反思了在系统开发过程中我们所做的共同假设，从而为可视化创作系统的未来研究方向提供参考。
