Interactive Mascot: A Scene-Centric Interaction Grammar for Data Visualizations
For visualization tool developers and researchers, this work provides a novel interaction abstraction that simplifies specifying interactions with semantic scene components, addressing a known bottleneck in scene-centric visualization systems.
Interactive Mascot introduces a scene-centric interaction grammar for data visualizations that models interactive behavior as information flow among four components and two contexts, enabling stateful interactions and direct manipulation of scene components. The grammar is implemented in Mascot.js, covers Vega-Lite's interaction design space, achieves comparable performance, and is shown to be learnable and usable in a qualitative study.
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.