November 2, 2021; Christina Warren

Congratulations to Rebecca Kehlbeck (Universität Konstanz, Work Group Visual Computing), Jochen Görtler (SFB-TRR 161 Project A01), Yunhai Wang (Shadong University) and SFB-TRR 161 project leader Oliver Deussen for winning a Best Paper Honorable Mention Award at the IEEE VIS 2021 conference for their paper "SpEuler: Semantics-preserving Euler Diagrams"

The IEEE VIS Best Paper Honorable Mention Award recognizes outstanding work from the pool of accepted papers. Best paper committees use a variety of criteria to select the best paper including potential impact to the community, the importance of any results obtained, technical challenges overcome. In their paper, Rebecca Kehlbeck, Jochen Görtler, Yunhai Wang and Oliver Deussen discuss how highly overlapping set-typed data can be visualized comprehensibly using Euler diagrams.


Creating comprehensible visualizations of highly overlapping set-typed data is a challenging task due to its complexity. To facilitate insights into set connectivity and to leverage semantic relations between intersections, we propose a fast two-step layout technique for Euler diagrams that are both well-matched and well-formed. Our method conforms to established form guidelines for Euler diagrams regarding semantics, aesthetics, and readability. First, we establish an initial ordering of the data, which we then use to incrementally create a planar, connected, and monotone dual graph representation. In the next step, the graph is transformed into a circular layout that maintains the semantics and yields simple Euler diagrams with smooth curves. When the data cannot be represented by simple diagrams, our algorithm always falls back to a solution that is not well-formed but still well-matched, whereas previous methods often fail to produce expected results. We show the usefulness of our method for visualizing set-typed data using examples from text analysis and infographics. Furthermore, we discuss the characteristics of our approach and evaluate our method against state-of-the-art methods.


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