Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

TLDR

A gap exists between visualization design guidelines and their practical application in tools, and no formal framework currently integrates empirical findings into automated design support. The authors aim to model visualization design knowledge as a set of constraints and to learn weights for soft constraints from experimental data. They express design knowledge as constraints in a testable, extensible form, implement it in Draco using Answer Set Programming, and enable recommendation of designs and easy augmentation with new constraints. They demonstrate how to build increasingly sophisticated automated design systems, including ones that learn weights directly from graphical perception experiments.

Abstract

There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.