Transient Visual Analytics

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dc.contributor.authorSchulz, Hans-Jörgen_US
dc.contributor.authorWeaver, Chrisen_US
dc.contributor.editorEl-Assady, Mennatallahen_US
dc.contributor.editorSchulz, Hans-Jörgen_US
dc.date.accessioned2024-05-21T08:30:13Z
dc.date.available2024-05-21T08:30:13Z
dc.date.issued2024
dc.description.abstractVisual 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Analytics (TVA), which complements this incremental addition of data with progressive removal of data as it becomes outdated, starts to clutter the visualization, and generally distracts from the data that is currently relevant to visual analysis.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.en_US
dc.description.sectionheadersProgressive Visual Analytics
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20241108
dc.identifier.isbn978-3-03868-253-0
dc.identifier.pages6 pages
dc.identifier.urihttps://doi.org/10.2312/eurova.20241108
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/eurova20241108
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visual Analytics; Computing methodologies → Progressive computation
dc.subjectHuman centered computing → Visual Analytics
dc.subjectComputing methodologies → Progressive computation
dc.titleTransient Visual Analyticsen_US
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