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dc.contributor.authorChuprikova, Ekaterinaen_US
dc.contributor.authorMacEachren, Alan M.en_US
dc.contributor.authorCron, Julianeen_US
dc.contributor.authorMeng, Liqiuen_US
dc.contributor.editorKai Lawonn and Noeska Smit and Lars Linsen and Robert Kosaraen_US
dc.date.accessioned2018-06-02T17:57:59Z
dc.date.available2018-06-02T17:57:59Z
dc.date.issued2018
dc.identifier.isbn978-3-03868-066-6
dc.identifier.urihttp://dx.doi.org/10.2312/eurorv3.20181141
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurorv320181141
dc.description.abstractThe Visual Analytics (VA) approach has become an important tool for gaining insights on various data sets. Thus, significant research has been conducted to integrate statistical methods in the interactive environment of VA where data visualization provides support to analysts in understanding and exploring the data. However, much of the data explored with VA is inherently uncertain due to limits of our knowledge about a phenomenon, randomness and indeterminism, and vagueness. The Bayesian Network (BN) is a graphical model that provides techniques for reasoning under conditions of uncertainty in a consistent and mathematically rigorous manner. While several software tools for visualizing and editing BNs exist, they have an evident shortcoming when spatial data. In this study, we propose a Visual Analytics-enabled BN approach for reasoning under uncertainty. We describe the implementation procedure using an example of heterogeneous data that includes locations of security surveillance cameras installed in public places.en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectI.3.3 [Computer Graphics]
dc.subjectPicture/Image Generation
dc.subjectLine and curve generation
dc.titleVisual Analytics-enabled Bayesian Network Approach to Reasoning about Public Camera Dataen_US
dc.description.seriesinformationEuroVis Workshop on Reproducibility, Verification, and Validation in Visualization (EuroRV3)
dc.description.sectionheadersSession 1
dc.identifier.doi10.2312/eurorv3.20181141
dc.identifier.pages1-5


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