Browsing by Author "Aupetit, Michael"

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    Augmented Intelligence with Interactive Voronoi Treemap for Scalable Grouping: a Usage Scenario with Wearable Data
    (The Eurographics Association, 2022) Abuthawabeh, Ala; Baggag, Abdelkader; Aupetit, Michael; Agus, Marco; Aigner, Wolfgang; Hoellt, Thomas
    Interactive Voronoi Treemaps have been proposed to support arrangement and grouping tasks of data with snippet image representations. They rely on time-consuming manual actions to group data and cannot display more than a hundred images without occlusion. We propose visualizations designed to manage images visibility, evaluate group homogeneity, and shorten grouping task completion time while keeping control. It is supported by an automatic classifier forming an augmented intelligence system to tackle arrangement and grouping tasks at scale. We propose the usage scenario of a clinician using Interactive Voronoi Treemaps to group wearable data based on sleep visual patterns.
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    Toward an Interactive Voronoi Treemap for Manual Arrangement and Grouping
    (The Eurographics Association, 2021) Abuthawabeh, Ala; Aupetit, Michael; Agus, Marco and Garth, Christoph and Kerren, Andreas
    Interactive spatial arrangement and grouping (A&G) of images is a critical step of the sense-making process. We argue that to support A&G tasks, a visual encoding idiom should avoid clutter, show groups explicitly, and maximize the use of space while allowing free positioning. None of the existing interactive idioms supporting A&G tasks optimizes all these criteria at once. We propose and implement an interactive Voronoi treemap for A&G that fulfills all these requirements. The cells representing groups or objects can be dragged or clicked to arrange objects and groups and to create, merge, split, expand, or collapse groups. We present a usage scenario for an art quiz game and a comparative analysis of our approach to the recent Piling.js library for a categorization task of HiC data images. We discuss limitations and future work.