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 Urania, 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 Urania 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，是解读数据分析结果的一种直观有效的方法。然而，创作数据动画并非易事。一个合格的数据动画不仅需要提供正确的分析结果，还需确保数据流和动画的连贯性。现有的动画创作工具要么侧重于利用算法基于用户提供的图表自动生成动画，要么通过构建图形用户界面为用户提供无需编程的创作环境。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。为弥补这一不足，我们提出了乌拉尼亚，一款支持数据动画设计与生成的智能化创作工具。它利用新颖的数据查询分解模型，使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。该初始数据动画可通过丰富的交互进行优化，同时借助反馈机制，根据用户的知识和偏好更新分解模型。我们的系统能生成由一组底层数据操作驱动的可视化动画序列。该系统支持单位可视化，可将每个数据项映射到独特的视觉标记。通过案例研究、性能验证及受控用户实验等一系列评估，我们验证了乌拉尼亚的有效性。