Automated Visualization for Visual Analytics: Trends, Challenges, and Opportunities

Abstract

Visualization, as a major approach of visual analytics, involves many human interaction techniques, especially in terms of how individuals communicate, comprehend, and interpret information. Creating visualizations is a tedious process and requires skill, but automatic data visualization technologies have made it easier to create visualizations. They completely changed the landscape of data analysis and decision-making processes. As the demand for effective and efficient visualization solutions grows across diverse sectors, researchers and practitioners have developed a plethora of autonomous systems aimed at transforming raw data into meaningful visual representations. This paper investigates the methodologies utilized by these systems, categorizing them based on machine learning approaches combined with various data inputs, template-based approach, and other technique/algorithm-based approach. We collected 31 top-tier journal papers in the field and shed light on the diverse techniques employed in generating visualizations automatically, enhancing our understanding of their capabilities, compatibility, and usability across various contexts. Our survey aims to provide insights into the strengths, limitations, and potential areas for future exploration in automatic data visualization, offering guidance to practitioners, researchers, and developers in selecting appropriate techniques for their specific needs and datasets. By systematically examining these systems and pinpointing areas for improvement, we contribute to the advancement and refinement of automatic data visualization methodologies, fostering progress in this dynamically evolving domain.

Keywords: Automation, Infographic, Data Visualization, Dashboard, Machine Learning, Artificial Intelligence, Natural Language.

DOI: 10.54941/ahfe1005503