Shaping unlimited patterns: A vision for state-of-the-art visual scalability

Abstract

Scaling data visualizations to represent large data sets remains one of the top challenges from the last decade. The fast growing amount of data is facing limitations of human perception and of technology as bottlenecks to exploring hidden patterns. This literature review summarizes the boundaries of visual scalability and their influencing factors, addressing state-of-the-art challenges. In a semantic differential approach, contrary characteristics were studied to support the classification of low and highly scalable data visualizations. As a result, the literature supports a vision of a shift toward adapting the taxonomy of visual scalability facing emergent technologies.