EuroVA2025

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https://diglib.eg.org/handle/10.2312/3607188

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Browsing EuroVA2025 by Subject "Human centered computing → Visualization"

Now showing 1 - 2 of 2
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    Evaluating Autoencoders for Parametric and Invertible Multidimensional Projections
    (The Eurographics Association, 2025) Dennig, Frederik L.; Geyer, Nina; Blumberg, Daniela; Metz, Yannick; Keim, Daniel A.; Schulz, Hans-Jörg; Villanova, Anna
    Recently, neural networks have gained attention for creating parametric and invertible multidimensional data projections. Parametric projections allow for embedding previously unseen data without recomputing the projection as a whole, while invertible projections enable the generation of new data points. However, these properties have never been explored simultaneously for arbitrary projection methods. We evaluate three autoencoder (AE) architectures for creating parametric and invertible projections. Based on a given projection, we train AEs to learn a mapping into 2D space and an inverse mapping into the original space. We perform a quantitative and qualitative comparison on four datasets of varying dimensionality and pattern complexity using t-SNE. Our results indicate that AEs with a customized loss function can create smoother parametric and inverse projections than feed-forward neural networks while giving users control over the strength of the smoothing effect.
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    Towards Integrating Visual Analytics in Multi-Perspective Conformance Checking: A Call to Action
    (The Eurographics Association, 2025) Linden, Sanne van der; Filipov, Velitchko Andreev; Pufahl, Luise; Miksch, Silvia; Elzen, Stef van den; Schulz, Hans-Jörg; Villanova, Anna
    The research fields of Process Mining (PM) and Visual Analytics (VA) can mutually benefit from each other by combining their strengths. PM tasks include process discovery, enhancement, and conformance checking. This paper focuses on conformance checking, where the event log is compared against a reference model to identify potential deviations in process behavior. Conformance checking is often limited to analyzing the control flow (i.e., sequences of activities), while other relevant perspectives present in the data, such as resources and time, are frequently overlooked. These additional perspectives are crucial to form a holistic understanding of deviations and their underlying causes. To address these limitations, we propose a conceptual framework and explore future opportunities for integrating VA with PM to support conformance checking from multiple perspectives. Our contribution emphasizes interactive visualization and analysis for a more flexible and iterative conformance checking process by, for example, allowing to dynamically refine and define additional constraints based on insights from multiple perspectives and making all deviations explainable and understandable.