Browsing by Author "Pufahl, Luise"

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    Designing and Evaluating a Structural Model for Conformance Checking Visualizations
    (The Eurographics Association, 2024) Klessascheck, Finn; Knoche, Tom; Pufahl, Luise; Arleo, Alessio; van den Elzen, Stef; von Landesberger, Tatiana; Rehse, Jana-Rebecca; Pufahl, Luise; Zerbato, Francesca
    Conformance checking is one of the main operations of process mining with which enterprises can analyse their business processes and consider whether their behaviour follows the desired one, or whether and where it deviates. In order to meaningfully present the results of this family of techniques, an appropriate and accessible visual interaction platform is necessary. However, existing work has focussed primarily on technical aspects, and less on user concerns. To this end, we combine visual analytics concepts with conformance checking. We derive a structural model from literature, which summarizes conformance checking applications, and helps in deriving descriptive models for concrete analyses. We implement our concept prototypically, and evaluate it in a user study, thereby showing the structural model's ability to help in creating accessible and appropriate visualizations for conformance checking.
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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.