On Quality Indicators for Progressive Visual Analytics

dc.contributor.authorAngelini, Marcoen_US
dc.contributor.authorMay, Thorstenen_US
dc.contributor.authorSantucci, Giuseppeen_US
dc.contributor.authorSchulz, Hans-Jörgen_US
dc.contributor.editorLandesberger, Tatiana von and Turkay, Cagatayen_US
dc.date.accessioned2019-06-02T18:19:21Z
dc.date.available2019-06-02T18:19:21Z
dc.date.issued2019
dc.description.abstractA key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough to cut a long-running computation short and to proceed. To aid in this process, we propose ten fundamental quality indicators that can be computed and displayed to gain a better understanding of the progress of the progression and of the stability and certainty of an intermediate outcome. We further highlight the use of these fundamental indicators to derive other quality indicators, and we show how to apply the indicators in two use cases.en_US
dc.description.sectionheadersVisual Analytics Methods
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20191120
dc.identifier.isbn978-3-03868-087-1
dc.identifier.pages25-29
dc.identifier.urihttps://doi.org/10.2312/eurova.20191120
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20191120
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectComputing methodologies
dc.subjectProgressive computation
dc.titleOn Quality Indicators for Progressive Visual Analyticsen_US
Files
Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
025-029.pdf
Size:
1.17 MB
Format:
Adobe Portable Document Format
Collections