Browsing by Author "May, Thorsten"

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    On Quality Indicators for Progressive Visual Analytics
    (The Eurographics Association, 2019) Angelini, Marco; May, Thorsten; Santucci, Giuseppe; Schulz, Hans-Jörg; Landesberger, Tatiana von and Turkay, Cagatay
    A key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough to cut a long-running computation short and to proceed. To aid in this process, we propose ten fundamental quality indicators that can be computed and displayed to gain a better understanding of the progress of the progression and of the stability and certainty of an intermediate outcome. We further highlight the use of these fundamental indicators to derive other quality indicators, and we show how to apply the indicators in two use cases.
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    PAVED: Pareto Front Visualization for Engineering Design
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Cibulski, Lena; Mitterhofer, Hubert; May, Thorsten; Kohlhammer, Jörn; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto-optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most-preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED, an interactive parallel coordinates visualization for exploration of multi-criteria alternatives. We reflect on our user-centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real-world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi-criteria optimization problems in engineering design or alternative domains.