Draco 2

AI Research Visualization Opensource Platform

July 2022 - Present

A constraint-based system for visualization recommendation. It encodes design knowledge as logical rules and uses a renderer-agnostic format, so researchers and practitioners can extend and validate chart designs in a computational way.

Developed in collaboration with

University of Vienna

University of Washington

University of Maryland

Carnegie Mellon University

Creating effective visualizations requires a deep understanding of design theory, yet most automated tools are rigid, making it difficult to customize them with new knowledge. They often treat design as a fixed template, which fails to capture the nuanced principles that guide human perception. This turns what should be an evolving practice into a static one, disconnected from ongoing research.

Draco 2 approaches this problem differently by treating visualization design not as a set of templates, but as a system of logical constraints. The core innovation is a generic, renderer-agnostic specification format that decouples the abstract principles of good design from any single rendering library. This allows the system to reason about the effectiveness of a chart—why certain choices work and others don’t—in a formal, computational way.

Using an Answer Set Programming solver like Clingo , the framework can recommend optimal designs or validate existing ones against its knowledge base of design rules. By making design expertise computational and extensible, the project aims to democratize effective data visualization. This research, recognized with a Best Short Paper Honorable Mention Award at IEEE VIS 2023, represents a step towards intelligent tools that can not only generate charts but also explain the reasoning behind them.

Stack

While the problem is more important than the tools, the tech stack tells a story about the project's architecture and trade-offs. Here's what this project is built on: