Browsing by Author "Sedlmair, Michael"

Now showing 1 - 4 of 4
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    netflower: Dynamic Network Visualization for Data Journalists
    (The Eurographics Association and John Wiley & Sons Ltd., 2019) Stoiber, Christina; Rind, Alexander; Grassinger, Florian; Gutounig, Robert; Goldgruber, Eva; Sedlmair, Michael; Emrich, Štefan; Aigner, Wolfgang; Gleicher, Michael and Viola, Ivan and Leitte, Heike
    Journalists need visual interfaces that cater to the exploratory nature of their investigative activities. In this paper, we report on a four-year design study with data journalists. The main result is netflower, a visual exploration tool that supports journalists in investigating quantitative flows in dynamic network data for story-finding. The visual metaphor is based on Sankey diagrams and has been extended to make it capable of processing large amounts of input data as well as network change over time. We followed a structured, iterative design process including requirement analysis and multiple design and prototyping iterations in close cooperation with journalists. To validate our concept and prototype, a workshop series and two diary studies were conducted with journalists. Our findings indicate that the prototype can be picked up quickly by journalists and valuable insights can be achieved in a few hours. The prototype can be accessed at: http://netflower.fhstp.ac.at/
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    SepEx: Visual Analysis of Class Separation Measures
    (The Eurographics Association, 2020) Bernard, Jürgen; Hutter, Marco; Zeppelzauer, Matthias; Sedlmair, Michael; Munzner, Tamara; Turkay, Cagatay and Vrotsou, Katerina
    Class separation is an important concept in machine learning and visual analytics. However, the comparison of class separation for datasets with varying dimensionality is non-trivial, given a) the various possible structural characteristics of datasets and b) the plethora of separation measures that exist. Building upon recent findings in visualization research about the qualitative and quantitative evaluation of class separation for 2D dimensionally reduced data using scatterplots, this research addresses the visual analysis of class separation measures for high-dimensional data. We present SepEx, an interactive visualization approach for the assessment and comparison of class separation measures for multiple datasets. SepEx supports analysts with the comparison of multiple separation measures over many high-dimensional datasets, the effect of dimensionality reduction on measure outputs by supporting nD to 2D comparison, and the comparison of the effect of different dimensionality reduction methods on measure outputs. We demonstrate SepEx in a scenario on 100 two-class 5D datasets with a linearly increasing amount of separation between the classes, illustrating both similarities and nonlinearities across 11 measures.
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    Visual Planning and Analysis of Latin Formation Dance Patterns
    (The Eurographics Association, 2023) Beck, Samuel; Doerr, Nina; Schmierer, Fabian; Sedlmair, Michael; Koch, Steffen; Gillmann, Christina; Krone, Michael; Lenti, Simone
    Latin formation dancing is a team sport in which up to eight couples perform a coordinated choreography. A central part are the patterns formed by the dancers on the dance floor and the transitions between them. Planning and practicing patterns are some of the most challenging aspects of Latin formation dancing. Interactive visualization approaches can support instructors as well as dancers in tackling these challenges. We present a web-based visualization prototype that assists with the planning, training, and analysis of patterns. Its design was iteratively developed with the involvement of experienced formation instructors. The interface offers views of the dancers' positions and orientations, pattern transitions, poses, and analytical information like dance floor utilization and movement distances. In a first expert study with formation instructors, the prototype was well received.
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    VMV 2021: Frontmatter
    (The Eurographics Association, 2021) Andres, Bjoern; Campen, Marcel; Sedlmair, Michael; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael