A Taxonomy of Attribute Scoring Functions

dc.contributor.authorSchmid, Jennyen_US
dc.contributor.authorBernard, Jürgenen_US
dc.contributor.editorVrotsou, Katerina and Bernard, Jürgenen_US
dc.date.accessioned2021-06-12T11:22:13Z
dc.date.available2021-06-12T11:22:13Z
dc.date.issued2021
dc.description.abstractShifting the analysis from items to the granularity of attributes is a promising approach to address complex decision-making problems. In this work, we study attribute scoring functions (ASFs), which transform values from data attributes to numerical scores. As the output of ASFs for different attributes is always comparable and scores carry user preferences, ASFs are particularly useful for analysis goals such as multi-attribute ranking, multi-criteria optimization, or similarity modeling. However, non-programmers cannot yet fully leverage their individual preferences on attribute values, as visual analytics (VA) support for the creation of ASFs is still in its infancy, and guidelines for the creation of ASFs are missing almost entirely. We present a taxonomy of eight types of ASFs and an overview of tools for the creation of ASFs as a result of an extensive literature review. Both the taxonomy and the tools overview have descriptive power, as they represent and combine non-visual math and statistics perspectives with the VA perspective. We underpin the usefulness of VA support for broader user groups in real-world cases for all eight types of ASFs, unveil missing VA support for the ASF creation, and discuss the integration of ASF in VA workflows.en_US
dc.description.sectionheadersVA Applications and Workflows
dc.description.seriesinformationEuroVis Workshop on Visual Analytics (EuroVA)
dc.identifier.doi10.2312/eurova.20211095
dc.identifier.isbn978-3-03868-150-2
dc.identifier.pages31-35
dc.identifier.urihttps://doi.org/10.2312/eurova.20211095
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurova20211095
dc.publisherThe Eurographics Associationen_US
dc.titleA Taxonomy of Attribute Scoring Functionsen_US
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