DIVI: Dynamically Interactive Visualization
Not on the map. Why?
Abstract (arXiv)
Dynamically Interactive Visualization (DIVI) is a novel approach for orchestrating interactions within and across static visualizations. DIVI deconstructs Scalable Vector Graphics charts at runtime to infer content and coordinate user input, decoupling interaction from specification logic. This decoupling allows interactions to extend and compose freely across different tools, chart types, and analysis goals. DIVI exploits positional relations of marks to detect chart components such as axes and legends, reconstruct scales and view encodings, and infer data fields. DIVI then enumerates candidate transformations across inferred data to perform linking between views. To support dynamic interaction without prior specification, we introduce a taxonomy that formalizes the space of standard interactions by chart element, interaction type, and input event. We demonstrate DIVI's usefulness for rapid data exploration and analysis through a usability study with 13 participants and a diverse gallery of dynamically interactive visualizations, including single chart, multi-view, and cross-tool configurations.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Oct 2023.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 2.
5 papers.
- 2024From Data to Story: Towards Automatic Animated Data Video Creation with LLM-based Multi-Agent Systemscited by 16
- 2025DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Textcited by 4
- 2025Challenges & Opportunities with LLM-Assisted Visualization Retargetingcited by 1
- 2026From Static to Interactive: Authoring Interactive Visualizations via Natural Languagecited by 0
- 2026Chart2SVG: Editable SVG Generation from Raster Chart Imagescited by 0
- 2026Chart2SVG: Editable SVG Generation from Raster Chart Images27 Aug 2026
- 2026From Static to Interactive: Authoring Interactive Visualizations via Natural Language25 Jan 2026
- 2025Challenges & Opportunities with LLM-Assisted Visualization Retargeting2 Jul 2025
- 2025DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text29 Mar 2025
- 2024From Data to Story: Towards Automatic Animated Data Video Creation with LLM-based Multi-Agent Systems7 Aug 2024