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    The Human User in Progressive Visual Analytics

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    Date
    2019
    Author
    Micallef, Luana
    Schulz, Hans-Jörg ORCID
    Angelini, Marco ORCID
    Aupetit, Michaël ORCID
    Chang, Remco ORCID
    Kohlhammer, Jörn ORCID
    Perer, Adam ORCID
    Santucci, Giuseppe
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    Abstract
    The amount of generated and analyzed data is ever increasing, and processing such large data sets can take too long in situations where time-to-decision or fluid data exploration are critical. Progressive visual analytics (PVA) has recently emerged as a potential solution that allows users to analyze intermediary results during the computation without waiting for the computation to complete. However, there has been limited consideration on how these techniques impact the user. Based on discussions from a Dagstuhl seminar held in October 2018, this paper characterizes PVA users by their common roles, their main tasks, and their distinct focus of analysis. It further discusses cognitive biases that play a particular role in PVA. This work will help PVA visualization designers in devising systems that are tailored for their specific target users and their characteristics.
    BibTeX
    @inproceedings {10.2312:evs.20191164,
    booktitle = {EuroVis 2019 - Short Papers},
    editor = {Johansson, Jimmy and Sadlo, Filip and Marai, G. Elisabeta},
    title = {{The Human User in Progressive Visual Analytics}},
    author = {Micallef, Luana and Schulz, Hans-Jörg and Angelini, Marco and Aupetit, Michaël and Chang, Remco and Kohlhammer, Jörn and Perer, Adam and Santucci, Giuseppe},
    year = {2019},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-090-1},
    DOI = {10.2312/evs.20191164}
    }
    URI
    https://doi.org/10.2312/evs.20191164
    https://diglib.eg.org:443/handle/10.2312/evs20191164
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA