TailVis: Expressive Chart Refinement Preserving Data-Binding Integrity

2026-07-28

AI Summary

This work addresses the limitations of existing data visualization tools, which often fail to support fine-grained visual customization while preserving data binding, thereby forcing users to resort to external graphic editors. To bridge this gap, we present TailVis, a system that extends the InfoVis reference model to encompass a post-rendering design phase. TailVis integrates element-level direct selection, natural language input, and dynamic GUI controls, augmented by referential interactions that enable data-aware, open-ended editing. The system maintains data-binding integrity through a dedicated preservation mechanism, employs an extensible selection model, and supports iterative design via provenance-aware history management. User studies demonstrate that TailVis significantly reduces repetitive operations and effectively enables expressive, data-consistent chart refinement within a unified environment.

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.

Problem

• data-binding integrity
• visual refinement
• visualization authoring
• static visualization
• expressive customization

Innovation

• data-binding integrity
• expressive refinement
• deictic interaction
• visualization authoring
• provenance history