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Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes

Zhicheng Liu, Chen Chen, John Hooker

TL;DR

MSC offers a unified computational representation for data-visualization scenes by coupling a generic object model of semantic components with a comprehensive set of generative and modificative operations. It enables three core applications—interactive authoring, deconstruction/reuse, and animation augmentation—through Mascot.js, a practical JavaScript implementation that serializes scenes to JSON and supports SVG/WebGL rendering. The framework captures detailed structural elements (marks, groups, encodings, layouts, constraints, and view configuration) and procedural steps (initialize, join, encode, layout, constrain, and view adjustments) to build and adapt visualizations. By contrast with DOM-based, declarative, and algebraic frameworks, MSC emphasizes a bottom-up, scene-centric approach that facilitates direct manipulation, rapid iteration, and robust reuse of visualization components, with future work on runtime interactions and knowledge extraction from MSC descriptions.

Abstract

Various data visualization applications such as reverse engineering and interactive authoring require a vocabulary that describes the structure of visualization scenes and the procedure to manipulate them. A few scene abstractions have been proposed, but they are restricted to specific applications for a limited set of visualization types. A unified and expressive model of data visualization scenes for different applications has been missing. To fill this gap, we present Manipulable Semantic Components (MSC), a computational representation of data visualization scenes, to support applications in scene understanding and augmentation. MSC consists of two parts: a unified object model describing the structure of a visualization scene in terms of semantic components, and a set of operations to generate and modify the scene components. We demonstrate the benefits of MSC in three applications: visualization authoring, visualization deconstruction and reuse, and animation specification.

Manipulable Semantic Components: a Computational Representation of Data Visualization Scenes

TL;DR

MSC offers a unified computational representation for data-visualization scenes by coupling a generic object model of semantic components with a comprehensive set of generative and modificative operations. It enables three core applications—interactive authoring, deconstruction/reuse, and animation augmentation—through Mascot.js, a practical JavaScript implementation that serializes scenes to JSON and supports SVG/WebGL rendering. The framework captures detailed structural elements (marks, groups, encodings, layouts, constraints, and view configuration) and procedural steps (initialize, join, encode, layout, constrain, and view adjustments) to build and adapt visualizations. By contrast with DOM-based, declarative, and algebraic frameworks, MSC emphasizes a bottom-up, scene-centric approach that facilitates direct manipulation, rapid iteration, and robust reuse of visualization components, with future work on runtime interactions and knowledge extraction from MSC descriptions.

Abstract

Various data visualization applications such as reverse engineering and interactive authoring require a vocabulary that describes the structure of visualization scenes and the procedure to manipulate them. A few scene abstractions have been proposed, but they are restricted to specific applications for a limited set of visualization types. A unified and expressive model of data visualization scenes for different applications has been missing. To fill this gap, we present Manipulable Semantic Components (MSC), a computational representation of data visualization scenes, to support applications in scene understanding and augmentation. MSC consists of two parts: a unified object model describing the structure of a visualization scene in terms of semantic components, and a set of operations to generate and modify the scene components. We demonstrate the benefits of MSC in three applications: visualization authoring, visualization deconstruction and reuse, and animation specification.
Paper Structure (30 sections, 14 figures, 1 table)

This paper contains 30 sections, 14 figures, 1 table.

Table of Contents

  1. Introduction
  2. Manipulable Semantic Components in a Diverging Stacked Bar Chart: An Example
  3. Object Model: Semantic Components
  4. Primary Visual Elements: Marks, Vertices & Segments
  5. Data Scope
  6. Primary Visual Elements: Groups
  7. Auxiliary Visual Elements
  8. Visual Encodings and Scales
  9. Algorithmic Layouts
  10. Relational Constraints
  11. View Configuration
  12. Alternative Scene Descriptions
  13. Procedure: Generative & Modificative Operations
  14. Generative Operations: Initialize Data and Elements
  15. Generative Operations: Join Visual Elements with Data
  16. ...and 15 more sections

Figures (14)

  • Figure 1: Manipulable semantic components in a diverging stacked bar chart. The scene abstraction not only captures hierarchical relationships between objects but also relationships like constraints and encodings.
  • Figure 2: Creating a diverging bar chart using the MSC operations.
  • Figure 3: Marks supported in in MSC and derivation of marks.
  • Figure 4: Example glyphs in (a) a violin plot, (b) the "Brain drain" visualization accurat_brain_2014, (c) a bullet chart, and (d) a box-and-whisker plot.
  • Figure 5: (a) A collection of rectangles forms the main chart content of a bar chart; (b) a collection of glyphs forms that of a box plot.
  • ...and 9 more figures

Theorems & Definitions (5)

  • Definition 1: Mark Type
  • Definition 2: Glyph
  • Definition 3: Glyph Type
  • Definition 4: Collection
  • Definition 5: Collection Type