Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data (AI4VIS). We define visualization data as the digital representations of visualizations in computers and focus on data visualization (e.g., charts and infographics). We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.
翻译:与文本和图像等其他数据格式类似,视觉化越来越多地被人工智能(AI)技术所创造、储存、共享和(再)使用。在这次调查中,我们探索了将视觉化正规化为新兴数据格式的基本愿景,并审查了最近在将人工智能技术应用于可视化数据方面的进展(AI4VIS)。我们把可视化数据定义为计算机中可视化的数字表示和数据可视化(例如图表和图表)的焦点。我们把我们的调查建立在计算机科学10个不同领域的材料之上,以期查明重要的共同利益。我们所产生的分类围绕“什么是可视化数据”及其表述方式、为什么和如何将人工智能应用于可视化数据进行组织。我们强调研究人员在视觉化数据中应用的一系列共同任务,并详细讨论为完成这些任务而开发的人工智能方法。我们根据我们的文献审查,讨论了有关可视化数据的管理和利用的若干重要研究问题,以及AI在支持这些进程中的作用。我们制作了调查论文和相关材料清单,供在线查阅。