A Survey on Progressive Visualization

Abstract

Fig. 1: Overview of our taxonomy with its four major categories: visualization, progressive processing, data domain, and visual update pattern. The visualization categories are based on previous taxonomies [47, 65, 85]. The progressive processing categories are an extension of the progressive visual analytics characterization [5], adding a new "Custom chunking" variant. The data domain categories cover the difference in visualization design if the end of the data or process is known or unknown. The fourth category is the visual update pattern which indicates how the visualizations are updated when new partial results are produced.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.