IEEE VIS 2024 Content: A Survey on Progressive Visualization

A Survey on Progressive Visualization

Alex Ulmer -

Marco Angelini -

Jean-Daniel Fekete -

Jörn Kohlhammerm -

Thorsten May -

DOI: 10.1109/TVCG.2023.3346641

Room: Bayshore I

2024-10-17T16:48:00ZGMT-0600Change your timezone on the schedule page
2024-10-17T16:48:00Z
Exemplar figure, described by caption below
Our new taxonomy for progressive visualisations. The categories of visualisation are based on previous taxonomies proposed by Shneiderman, Keim and Munzner. The categories of progressive processing represent an extension of the characterisation proposed by Angelini et al., with the addition of a new variant, termed 'custom chunking'. The categories of data domain address the implications of differing visualisation designs in the context of known and unknown data or process endpoints. The fourth category is visual update pattern, which indicates the manner in which visualisations are updated in response to the generation of new partial results.
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Keywords

Data visualization, Convergence, Visual analytics, Taxonomy Surveys, Rendering (computer graphics), Task analysis, Progressive Visual Analytics, Progressive Visualization, Taxonomy, State-of-the-Art Report, Survey

Abstract

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.