EuroVA2021
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Browsing EuroVA2021 by Author "b7736374-f807-4f21-82f0-e4a978fec7d3"
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Item EuroVa 2021: Frontmatter(The Eurographics Association, 2021) Bernard, Jürgen; Vrotsou, Katerina; Vrotsou, Katerina and Bernard, JürgenItem LFPeers: Temporal Similarity Search in Covid-19 Data(The Eurographics Association, 2021) Burmeister, Jan; Bernard, Jürgen; Kohlhammer, Jörn; Vrotsou, Katerina and Bernard, JürgenWhile there is a wide variety of visualizations and dashboards to help understand the data of the Covid-19 pandemic, hardly any of these support important analytical tasks, especially of temporal attributes. In this paper, we introduce a general concept for the analysis of temporal and multimodal data and the system LFPeers that applies this concept to the analysis of countries in a Covid-19 dataset. Our concept divides the analysis in two phases: a search phase to find the most similar objects to a target object before a time point t0, and an exploration phase to analyze this subset of objects after t0. LFPeers targets epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures.Item A Taxonomy of Attribute Scoring Functions(The Eurographics Association, 2021) Schmid, Jenny; Bernard, Jürgen; Vrotsou, Katerina and Bernard, JürgenShifting 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.