Datasets of Visualization for Machine Learning

TL;DR

This paper surveys existing visualization datasets for machine learning, analyzing their characteristics, challenges, and proposing a model to understand their diversity, aiming to guide future dataset development and standardization.

Contribution

It provides a comprehensive overview of visualization datasets, introduces a what-why-how model, and discusses challenges and future directions for dataset standardization and expansion.

Findings

Diverse data types and formats in visualization datasets

Limited availability of large-scale datasets

Challenges in standardization and dataset construction

Abstract

Datasets of visualization play a crucial role in automating data-driven visualization pipelines, serving as the foundation for supervised model training and algorithm benchmarking. In this paper, we survey the literature on visualization datasets and provide a comprehensive overview of existing visualization datasets, including their data types, formats, supported tasks, and openness. We propose a what-why-how model for visualization datasets, considering the content of the dataset (what), the supported tasks (why), and the dataset construction process (how). This model provides a clear understanding of the diversity and complexity of visualization datasets. Additionally, we highlight the challenges faced by existing visualization datasets, including the lack of standardization in data types and formats and the limited availability of large-scale datasets. To address these challenges, we suggest future research directions.