AI 中文总结
研究针对以场景为中心的可视化系统交互抽象与语义组件不匹配问题，提出交互式吉祥物语法。通过建模交互行为为组件间信息流及引入依赖图执行模型来实现。实现与Vega-Lite相当性能，涵盖其交互空间且支持多种交互，经用户研究语法可学习可用。
AI 中文摘要
以场景为中心的可视化系统将诸如标记、编码、布局和坐标轴等语义组件作为可直接操作的一等对象暴露出来。然而，现有的交互抽象大多基于事件流、信号和数据选择，而非语义场景组件。这种不匹配使得涉及场景组件的交互指定起来不那么自然，并限制了以场景为中心的可视化系统的表达能力。我们提出了交互式吉祥物（Interactive Mascot），一种用于数据可视化的以场景为中心的交互语法。它通过将交互行为建模为四个交互组件（触发器、响应器、评估器和更新器）以及两种上下文形式（事件上下文和状态上下文）之间的信息流，扩展了静态可视化的以场景为中心的表示。为实现这些语义，我们引入了一种依赖图执行模型，该模型使用与语义可视化组件相关联的可重用图模式将交互规范系统地转换为可执行的依赖图。我们在JavaScript库中实现了交互式吉祥物，并评估了其表达能力、性能和可用性。交互式吉祥物自然地涵盖了Vega-Lite的交互设计空间，同时还支持有状态交互、场景组件的直接操作和自由形式选择。它实现了与Vega-Lite相当的运行时性能，并且一项定性用户研究表明该语法对于交互创作是可学习和可用的。
英文摘要
Scene-centric visualization systems expose semantic components, such as marks, encodings, layouts, and axes, as first-class objects that can be directly manipulated. Existing interaction abstractions, however, are largely based on event streams, signals, and data selections rather than semantic scene components. This mismatch makes interactions involving scene components less natural to specify and limits the expressive power of scene-centric visualization systems. We present Interactive Mascot, a scene-centric interaction grammar for data visualizations. Interactive Mascot extends scene-centric representations for static visualizations by modeling interactive behavior as information flow among four interaction components (trigger, responder, evaluator, and updater) and two forms of context (event context and state context). To realize these semantics, we introduce a dependency-graph execution model that systematically transforms interaction specifications into executable dependency graphs using reusable graph patterns associated with semantic visualization components. We implement Interactive Mascot in the JavaScript library Mascot$.$js and evaluate its expressiveness, performance, and usability. Interactive Mascot naturally covers Vega-Lite's interaction design space while additionally supporting stateful interactions, direct manipulation of scene components, and freeform selection. It achieves runtime performance comparable to Vega-Lite, and a qualitative user study shows that the grammar is learnable and usable for interaction authoring.
