Datamation is designed to animate an analysis pipeline step by step, which is an intuitive and effective way to interpret the results from data analysis. However, creating a datamation is not easy. A qualified datamation needs to not only provide a correct analysis result but also ensure that the data flow and animation are coherent. Existing animation authoring tools focus on either leveraging algorithms to automatically generate an animation based on user-provided charts or building graphical user interfaces to provide a programming-free authoring environment for users. None of them are able to help users translate an analysis task into a series of data operations to form an analysis pipeline and visualize them as a datamation. To fill this gap, we introduce Datamator, an intelligent authoring tool developed to support datamation design and generation. It leverages a novel data query decomposition model to allow users to generate an initial datamation by simply inputting a data query in natural language. The initial datamation can be refined via rich interactions and a feedback mechanism is utilized to update the decomposition model based on user knowledge and preferences. Our system produces an animated sequence of visualizations driven by a set of low-level data actions. It supports unit visualizations, which provide a mapping from each data item to a unique visual mark. We demonstrate the effectiveness of Datamator via a series of evaluations including case studies, performance validation, and a controlled user study.

翻译：数据动画旨在逐步动画化分析流程，是直观且有效地解读数据分析结果的方式。然而，创建数据动画并非易事。合格的数据动画不仅需要提供正确的分析结果，还需确保数据流与动画的连贯性。现有动画创作工具要么专注于利用算法基于用户提供的图表自动生成动画，要么构建图形用户界面以提供无需编程的创作环境。但这些工具均无法帮助用户将分析任务转化为一系列数据操作以形成分析流程，并将其可视化为数据动画。为填补这一空白，我们提出Datamator——一款支持数据动画设计与生成的智能创作工具。该工具利用新颖的数据查询分解模型，用户仅需以自然语言输入数据查询即可生成初始数据动画。初始数据动画可通过丰富的交互进行精炼，并采用反馈机制根据用户知识与偏好更新分解模型。本系统生成由一组底层数据动作驱动的动画可视化序列，并支持单元可视化——即将每个数据项映射至独特的视觉标记。我们通过案例研究、性能验证及受控用户实验等系列评估，证明了Datamator的有效性。