A Survey on Progressive Visualization

Alex Ulmer (1, 2) , Marco Angelini (3) , Jean-Daniel Fekete (4) , Jörn Kohlhammer (1, 2) , Thorsten May (1, 2)

1 Fraunhofer IGD - Fraunhofer Institute for Computer Graphics Research [Darmstadt]
2 TU Darmstadt - Technische Universität Darmstadt - Technical University of Darmstadt
3 UNIROMA - Università degli Studi di Roma "La Sapienza" = Sapienza University [Rome]
4 AVIZ - Analysis and Visualization

Currently, growing data sources and long-running algorithms impede user attention and interaction with visual analytics applications. Progressive visualization (PV) and visual analytics (PVA) alleviate this problem by allowing immediate feedback and interaction with large datasets and complex computations, avoiding waiting for complete results by using partial results improving with time. Yet, creating a progressive visualization requires more effort than a regular visualization but also opens up new possibilities, such as steering the computations towards more relevant parts of the data, thus saving computational resources. However, there is currently no comprehensive overview of the design space for progressive visualization systems. We surveyed the related work of PV and derived a new taxonomy for progressive visualizations by systematically categorizing all PV publications that included visualizations with progressive features. Progressive visualizations can be categorized by well-known visualization taxonomies, but we also found that progressive visualizations can be distinguished by the way they manage their data processing, data domain, and visual update. Furthermore, we identified key properties such as uncertainty, steering, visual stability, and real-time processing that are significantly different with progressive applications. We also collected evaluation methodologies reported by the publications and conclude with statistical findings, research gaps, and open challenges. A continuously updated visual browser of the survey data is available at visualsurvey.net/pva.

Keywords: Progressive Visual Analytics; Progressive Visualization; Taxonomy; State-of-the-Art Report; Survey

Domains: Human-Computer Interaction [cs.HC]

Alex Ulmer, Marco Angelini, Jean-Daniel Fekete, Jörn Kohlhammer, Thorsten May. A Survey on Progressive Visualization. IEEE Transactions on Visualization and Computer Graphics, In press, ⟨10.1109/TVCG.2023.3346641⟩. ⟨hal-04361344⟩