Wang Chenglong, Thompson John, Lee Bongshin
IEEE Trans Vis Comput Graph. 2024 Jan;30(1):1128-1138. doi: 10.1109/TVCG.2023.3326585. Epub 2023 Dec 25.
With most modern visualization tools, authors need to transform their data into tidy formats to create visualizations they want. Because this requires experience with programming or separate data processing tools, data transformation remains a barrier in visualization authoring. To address this challenge, we present a new visualization paradigm, concept binding, that separates high-level visualization intents and low-level data transformation steps, leveraging an AI agent. We realize this paradigm in Data Formulator, an interactive visualization authoring tool. With Data Formulator, authors first define data concepts they plan to visualize using natural languages or examples, and then bind them to visual channels. Data Formulator then dispatches its AI-agent to automatically transform the input data to surface these concepts and generate desired visualizations. When presenting the results (transformed table and output visualizations) from the AI agent, Data Formulator provides feedback to help authors inspect and understand them. A user study with 10 participants shows that participants could learn and use Data Formulator to create visualizations that involve challenging data transformations, and presents interesting future research directions.
对于大多数现代可视化工具而言，作者需要将他们的数据转换为整洁的格式，以创建他们想要的可视化效果。由于这需要编程经验或单独的数据处理工具，数据转换仍然是可视化创作中的一个障碍。为了应对这一挑战，我们提出了一种新的可视化范式——概念绑定，它利用人工智能代理将高级可视化意图与低级数据转换步骤分离开来。我们在交互式可视化创作工具Data Formulator中实现了这一范式。使用Data Formulator时，作者首先使用自然语言或示例定义他们计划可视化的数据概念，然后将它们绑定到视觉通道。Data Formulator随后派遣其人工智能代理自动转换输入数据，以呈现这些概念并生成所需的可视化效果。在展示人工智能代理的结果（转换后的表格和输出可视化效果）时，Data Formulator提供反馈以帮助作者检查和理解这些结果。一项针对10名参与者的用户研究表明，参与者能够学习并使用Data Formulator来创建涉及具有挑战性的数据转换的可视化效果，并提出了有趣的未来研究方向。
