Universität Stuttgart

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Institute: search.filters.UBSInstitut.Institut für Visualisierung und Interaktive SystemeAuthor: search.filters.author.Abdelaal, Moataz

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    Visual analytics for nonlinear programming in robot motion planning
    (2022) Hägele, David; Abdelaal, Moataz; Oguz, Ozgur S.; Toussaint, Marc; Weiskopf, Daniel
    Nonlinear programming is a complex methodology where a problem is mathematically expressed in terms of optimality while imposing constraints on feasibility. Such problems are formulated by humans and solved by optimization algorithms. We support domain experts in their challenging tasks of understanding and troubleshooting optimization runs of intricate and high-dimensional nonlinear programs through a visual analytics system. The system was designed for our collaborators’ robot motion planning problems, but is domain agnostic in most parts of the visualizations. It allows for an exploration of the iterative solving process of a nonlinear program through several linked views of the computational process. We give insights into this design study, demonstrate our system for selected real-world cases, and discuss the extension of visualization and visual analytics methods for nonlinear programming.
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    ItemOpen Access
    Multi-time scale dynamic graph visualization
    (2017) Abdelaal, Moataz
    Dynamic graphs build complex data structures composed of vertices, edges, and time steps. Visualizing these evolving structures is a challenging task when we are not only interested in the dynamics based on a fixed time granularity, but also in exploring the subsequences at multiple of those time granularities. In this thesis, we introduce a multi-timescale dynamic graph visualization. The dynamic graph is displayed with interleaved parallel edge splatting focusing on visual scalability to generate an overview of dynamic graph patterns first. Different time scales can then be displayed in a vertically stacked scale-to-space mapping showing finer time granularities in linked side-by-side views, which is in particular useful for comparison tasks. To obtain an uncluttered view of the evolving graph patterns, the data is first preprocessed by clustering and vertex ordering techniques. It is then plotted in a 1D bipartite layout, splatted, smoothed, and enhanced with contour lines for perceptual augmentation. Inner- and inter-scale comparisons are supported visually and algorithmically.