Schulz, Hans-JörgWeaver, ChrisEl-Assady, MennatallahSchulz, Hans-Jörg2024-05-212024-05-212024978-3-03868-253-0https://doi.org/10.2312/eurova.20241108https://diglib.eg.org/handle/10.2312/eurova20241108Visual Analytics often utilizes progression as a means to overcome the challenges presented by large amounts of data or extensive computations. In Progressive Visual Analytics (PVA), data gets chunked into smaller subsets, which are then processed independently, and subsequently added to a visualization that completes over time. We introduce Transient Visual Analytics (TVA), which complements this incremental addition of data with progressive removal of data as it becomes outdated, starts to clutter the visualization, and generally distracts from the data that is currently relevant to visual analysis. Through combinations of various progressive addition and removal strategies, and supported by suitable analogies for the analyst and the software engineer, TVA captures a variety of visual analysis scenarios and approaches that are not well captured by PVA alone.Attribution 4.0 International LicenseCCS Concepts: Human-centered computing → Visual Analytics; Computing methodologies → Progressive computationHuman centered computing → Visual AnalyticsComputing methodologies → Progressive computation10.2312/eurova.202411086 pages