Quality Metrics for Information Visualization

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dc.contributor.authorBehrisch, Michaelen_US
dc.contributor.authorBlumenschein, Michaelen_US
dc.contributor.authorKim, Nam Wooken_US
dc.contributor.authorShao, Linen_US
dc.contributor.authorEl-Assady, Mennatallahen_US
dc.contributor.authorFuchs, Johannesen_US
dc.contributor.authorSeebacher, Danielen_US
dc.contributor.authorDiehl, Alexandraen_US
dc.contributor.authorBrandes, Ulriken_US
dc.contributor.authorPfister, Hanspeteren_US
dc.contributor.authorSchreck, Tobiasen_US
dc.contributor.authorWeiskopf, Danielen_US
dc.contributor.authorKeim, Daniel A.en_US
dc.contributor.editorRobert S. Laramee and G. Elisabeta Marai and Michael Sedlmairen_US
dc.date.accessioned2018-06-02T17:52:02Z
dc.date.available2018-06-02T17:52:02Z
dc.date.issued2018
dc.description.abstractThe visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi- and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.en_US
dc.description.documenttypestar
dc.description.number3
dc.description.sectionheadersDealing with Scale
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume37
dc.identifier.doi10.1111/cgf.13446
dc.identifier.issn1467-8659
dc.identifier.pages625-662
dc.identifier.urihttps://doi.org/10.1111/cgf.13446
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf13446
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectVisualization [Human
dc.subjectcentered computing]
dc.subjectVisualization Techniques
dc.titleQuality Metrics for Information Visualizationen_US
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