Flint: A Semantic-driven Data Visualization Intermediate Language
Authors
Yunhai Wang (Renmin University of China), Kecheng Lu (Renmin University of China), Junhao Chen (Renmin University of China), Alper Sarikaya (Microsoft Corporation), Chenglong Wang (Microsoft Research)
Presentation
- Session
- Modern talking-visually
- Time
- Tuesday, Nov 10, 14:12 – 14:24 (US/Eastern) · session 13:00 – 14:30
- Location
- Hall America center
Links
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Keywords
LLM, semantic type, intermediate language
Abstract
We present Flint, an intermediate language that enables authors to create high-quality visualizations from concise, semantics-driven specifications without explicitly configuring low-level parameters such as scales, axes, and formatting. Unlike prior systems that infer default configurations from surface-level data representations, often producing brittle choices, Flint introduces a hierarchical data semantic model that allows users to specify the meanings of data fields structurally and helps the compiler derive appropriate visualization configurations. From a concise specification, the system generates and optimizes library-agnostic visualization configurations and translates them into complete, executable specifications for multiple target grammars, including Vega-Lite, Apache ECharts, and Chart.js. We demonstrate that Flint simplifies the authoring process without compromising on visual quality, and it is an effective intermediate language for both humans and AI agents to create visualizations.
For Practitioners
Data journalists AI agent developer Data analysts