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    • 38-Issue 3
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    Visual-Interactive Preprocessing of Multivariate Time Series Data

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    Date
    2019
    Author
    Bernard, Jürgen ORCID
    Hutter, Marco ORCID
    Reinemuth, Heiko
    Pfeifer, Hendrik
    Bors, Christian
    Kohlhammer, Jörn
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    Abstract
    Pre-processing is a prerequisite to conduct effective and efficient downstream data analysis. Pre-processing pipelines often require multiple routines to address data quality challenges and to bring the data into a usable form. For both the construction and the refinement of pre-processing pipelines, human-in-the-loop approaches are highly beneficial. This particularly applies to multivariate time series, a complex data type with multiple values developing over time. Due to the high specificity of this domain, it has not been subject to in-depth research in visual analytics. We present a visual-interactive approach for preprocessing multivariate time series data with the following aspects. Our approach supports analysts to carry out six core analysis tasks related to pre-processing of multivariate time series. To support these tasks, we identify requirements to baseline toolkits that may help practitioners in their choice. We characterize the space of visualization designs for uncertainty-aware pre-processing and justify our decisions. Two usage scenarios demonstrate applicability of our approach, design choices, and uncertainty visualizations for the six analysis tasks. This work is one step towards strengthening the visual analytics support for data pre-processing in general and for uncertainty-aware pre-processing of multivariate time series in particular.
    BibTeX
    @article {10.1111:cgf.13698,
    journal = {Computer Graphics Forum},
    title = {{Visual-Interactive Preprocessing of Multivariate Time Series Data}},
    author = {Bernard, Jürgen and Hutter, Marco and Reinemuth, Heiko and Pfeifer, Hendrik and Bors, Christian and Kohlhammer, Jörn},
    year = {2019},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13698}
    }
    URI
    https://doi.org/10.1111/cgf.13698
    https://diglib.eg.org:443/handle/10.1111/cgf13698
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    Eurographics Association copyright © 2013 - 2020 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
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