VMV2021

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https://diglib.eg.org/handle/10.2312/2633087

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Browsing VMV2021 by Subject "Applied computing"

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    CoSi: Visual Comparison of Similarities in High-Dimensional Data Ensembles
    (The Eurographics Association, 2021) Heim, Anja; Gröller, Eduard; Heinzl, Christoph; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Comparative analysis of multivariate datasets, e.g. of advanced materials regarding the characteristics of internal structures (fibers, pores, etc.), is of crucial importance in various scientific disciplines. Currently domain experts in materials science mostly rely on sequential comparison of data using juxtaposition. Our work assists domain experts to perform detailed comparative analyses of large ensemble data in materials science applications. For this purpose, we developed a comparative visualization framework, that includes a tabular overview and three detailed visualization techniques to provide a holistic view on the similarities in the ensemble. We demonstrate the applicability of our framework on two specific usage scenarios and verify its techniques using a qualitative user study with 12 material experts. The insights gained from our work represent a significant advancement in the field of comparative material analysis of high-dimensional data. Our framework provides experts with a novel perspective on the data and eliminates the need for time-consuming sequential exploration of numerical data.
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    Visualizing Temporal-Thematic Patterns in Text Collections
    (The Eurographics Association, 2021) Knabben, Moritz; Baumann, Martin; Blascheck, Tanja; Ertl, Thomas; Koch, Steffen; Andres, Bjoern and Campen, Marcel and Sedlmair, Michael
    Visualizing the temporal evolution of texts is relevant for many domains that seek to gain insight from text repositories. However, existing visualization methods for text collections do not show fine-grained temporal-thematic patterns. Therefore, we developed and analyzed a new visualization method that aims at uncovering such patterns. Specifically, we project texts to one dimension, which allows positioning texts in a 2D diagram of projection space and time. For projection, we employed two manifold learning algorithms: the self-organizing map (SOM) and UMAP. To assess the utility of our method, we experimented with real-world datasets and discuss the resulting visualizations. We find our method facilitates relating patterns and extracting associated texts beyond what is possible with previous techniques. We also conducted interviews with historians to show that our prototypical system supports domain experts in their analysis tasks.