A survey on automatic infographics and visualization recommendations
Sujia Zhu, Guodao Sun, Qi Jiang, Meng Zha, Ronghua Liang
Abstract
Automatic infographics generators employ machine learning algorithms/user-defined rules and visual embellishments into the creation of infographics. It is an emerging topic in the field of information visualization that has requirements in many sectors, such as dashboard design, data analysis, and visualization recommendation. The growing popularity of visual analytics in recent years brings increased attention to automatic infographics. This creates the need for a broad survey that reviews and assesses the significant advances in this field. Automatic tools aim to lower the barrier for visually analyzing data by automatically generating visualizations for analysts to search and make a choice, instead of manually specifying. This survey reviews and classifies automatic tools and papers of visualization recommendations into a set of application categories including network-graph visualizations, annotation visualizations, and storytelling visualization. More importantly, this report presents several challenges and promising directions for future work in the field of automatic infographics and visualization recommendations.