Empirically Measuring Soft Knowledge in Visualization

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dc.contributor.authorKijmongkolchai, Natchayaen_US
dc.contributor.authorAbdul-Rahman, Alfieen_US
dc.contributor.authorChen, Minen_US
dc.contributor.editorHeer, Jeffrey and Ropinski, Timo and van Wijk, Jarkeen_US
dc.date.accessioned2017-06-12T05:22:21Z
dc.date.available2017-06-12T05:22:21Z
dc.date.issued2017
dc.description.abstractIn this paper, we present an empirical study designed to evaluate the hypothesis that humans' soft knowledge can enhance the cost-benefit ratio of a visualization process by reducing the potential distortion. In particular, we focused on the impact of three classes of soft knowledge: (i) knowledge about application contexts, (ii) knowledge about the patterns to be observed (i.e., in relation to visualization task), and (iii) knowledge about statistical measures. We mapped these classes into three control variables, and used real-world time series data to construct stimuli. The results of the study confirmed the positive contribution of each class of knowledge towards the reduction of the potential distortion, while the knowledge about the patterns prevents distortion more effectively than the other two classes.en_US
dc.description.number3
dc.description.sectionheadersEvaluating Visualization
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume36
dc.identifier.doi10.1111/cgf.13169
dc.identifier.issn1467-8659
dc.identifier.pages073-085
dc.identifier.urihttps://doi.org/10.1111/cgf.13169
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13169
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.titleEmpirically Measuring Soft Knowledge in Visualizationen_US
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