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    LFPeers: Temporal Similarity Search in Covid-19 Data

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    Date
    2021
    Author
    Burmeister, Jan
    Bernard, Jürgen ORCID
    Kohlhammer, Jörn ORCID
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    Abstract
    While 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.
    BibTeX
    @inproceedings {10.2312:eurova.20211098,
    booktitle = {EuroVis Workshop on Visual Analytics (EuroVA)},
    editor = {Vrotsou, Katerina and Bernard, Jürgen},
    title = {{LFPeers: Temporal Similarity Search in Covid-19 Data}},
    author = {Burmeister, Jan and Bernard, Jürgen and Kohlhammer, Jörn},
    year = {2021},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-150-2},
    DOI = {10.2312/eurova.20211098}
    }
    URI
    https://doi.org/10.2312/eurova.20211098
    https://diglib.eg.org:443/handle/10.2312/eurova20211098
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    Eurographics Association copyright © 2013 - 2022 
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    System hosted at  Graz University of Technology.
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