Angelini, MarcoMay, ThorstenSantucci, GiuseppeSchulz, Hans-JörgLandesberger, Tatiana von and Turkay, Cagatay2019-06-022019-06-022019978-3-03868-087-1https://doi.org/10.2312/eurova.20191120https://diglib.eg.org:443/handle/10.2312/eurova20191120A 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.Humancentered computingVisual analyticsComputing methodologiesProgressive computation10.2312/eurova.2019112025-29