Data Augmentation for Visualization Design Knowledge Bases

TL;DR

This paper introduces data augmentation techniques to expand and improve visualization knowledge bases by generating and labeling new chart pairs, enhancing coverage and recommendation accuracy.

Contribution

It presents novel methods for generating and labeling chart pairs to augment visualization knowledge bases, addressing limitations of existing datasets.

Findings

Expanded corpus with thousands of new chart pairs

Improved feature coverage in the knowledge base

Enhanced chart recommendation performance

Abstract

Visualization knowledge bases enable computational reasoning and recommendation over a visualization design space. These systems evaluate design trade-offs using numeric weights assigned to different features (e.g., binning a variable). Feature weights can be learned automatically by fitting a model to a collection of chart pairs, in which one chart is deemed preferable to the other. To date, labeled chart pairs have been drawn from published empirical research results; however, such pairs are not comprehensive, resulting in a training corpus that lacks many design variants and fails to systematically assess potential trade-offs. To improve knowledge base coverage and accuracy, we contribute data augmentation techniques for generating and labeling chart pairs. We present methods to generate novel chart pairs based on design permutations and by identifying under-assessed features -- leading to an expanded corpus with thousands of new chart pairs, now in need of labels. Accordingly, we next compare varied methods to scale labeling efforts to annotate chart pairs, in order to learn updated feature weights. We evaluate our methods in the context of the Draco knowledge base, demonstrating improvements to both feature coverage and chart recommendation performance.