TailVis: Expressive Visualization Refinement Preserving Data-Binding Integrity
Authors
Yumin Song (Seoul National University), Seokhyeon Park (Seoul National University), Soohyun Lee (Seoul National University), Aeri Cho (Seoul National University), Hyeon Jeon (Seoul National University), John Joon Young Chung (Midjourney), Jinwook Seo (Seoul National University)
Presentation
- Session
- How can I do this myself?
- Time
- Thursday, Nov 12, 08:36 – 08:48 (US/Eastern) · session 08:00 – 09:30
- Location
- Hall America center
Keywords
Visualization authoring, customization, interaction techniques, presentation, graphic interfaces
Abstract
Creating static visualizations for presentations and publications requires granular refinements of visual details, even for simple charts. Existing data-driven visualization tools offer limited interactive control for such refinements, forcing users to export charts to external graphic editors and breaking the critical link between data and visual representation. To address this gap, we propose an extended InfoVis Reference Model to account for post-render design refinement. A formative study with 18 visualization practitioners and a follow-up survey of 35 respondents confirmed that this stage is pervasive yet unsupported in current practice. Based on these findings, we present TailVis, a visualization authoring system that enables expressive visual customization while preserving data-binding integrity. TailVis supports element-level direct selection and scope expansion, allowing users to define a data-aware scope ranging from a single mark to a data-driven category with a simple selection. For modifications beyond predefined controls, TailVis blends natural language input with dynamically generated GUI widgets, where deictic interaction lets users reference elements simply by clicking them, keeping even open-ended edits bound to the data. To support rigorous exploration and comparison of design alternatives, TailVis implements a provenance history that enables users to capture diverse design iterations while ensuring data-visual integrity. A user study with 12 participants verified that TailVis effectively supports expressive, granular refinement without sacrificing data binding, significantly reducing repetitive manual processes in an integrated environment.
For Practitioners
This paper targets practitioners who produce charts as final artifacts for presentations and publications: data journalists, data scientists and analysts, and researchers preparing publication-ready figures. The paper frames design refinement as a stage of the visualization process rather than a post-hoc cleanup step, and shows that it need not come at the cost of data binding. Practitioners gain a way to keep refining a chart without leaving their data-driven tool, while researchers and tool builders gain a model and a set of interaction techniques for studying and supporting this stage.