We present a grammar for expressing hypotheses in visual data analysis to formalize the previously abstract notion of "analysis tasks." Through the lens of our grammar, we lay the groundwork for how a user's data analysis questions can be operationalized and automated as a set of hypotheses (a hypothesis space). We demonstrate that our grammar-based approach for analysis tasks can provide a systematic method towards unifying three disparate spaces in visualization research: the hypotheses a dataset can express (a data hypothesis space), the hypotheses a user would like to refine or verify through analysis (an analysis hypothesis space), and the hypotheses a visualization design is capable of supporting (a visualization hypothesis space). We illustrate how the formalization of these three spaces can inform future research in visualization evaluation, knowledge elicitation, analytic provenance, and visualization recommendation by using a shared language for hypotheses. Finally, we compare our proposed grammar-based approach with existing visual analysis models and discuss the potential of a new hypothesis-driven theory of visual analytics.

翻译：我们提出了一种用于在可视化数据分析中表达假设的语法，旨在将此前“分析任务”这一抽象概念形式化。通过该语法的视角，我们为如何将用户的数据分析问题操作化并自动化为一组假设（即假设空间）奠定了基础。我们证明，这种基于语法的分析任务方法能够提供一种系统化的途径，用于统一可视化研究中三个原本分散的空间：数据集所能表达的假设（数据假设空间）、用户希望通过分析来细化或验证的假设（分析假设空间），以及可视化设计能够支持的假设（可视化假设空间）。我们阐释了如何通过共享假设语言，利用这三个空间的形式化来推动可视化评估、知识引导、分析溯源和可视化推荐等领域的未来研究。最后，我们将所提出的基于语法的方法与现有可视化分析模型进行比较，并探讨一种新的基于假设驱动的可视分析理论的潜力。