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• W3118943338 abstract "Event sequence data is increasingly available in various application domains, such as business process management, software engineering, or medical pathways. Processes in these domains are typically represented as process diagrams or flow charts. So far, various techniques have been developed for automatically generating such diagrams from event sequence data. An open challenge is the visual analysis of drift phenomena when processes change over time. In this article, we address this research gap. Our contribution is a system for fine-granular process drift detection and corresponding visualizations for event logs of executed business processes. We evaluated our system both on synthetic and real-world data. On synthetic logs, we achieved an average F-score of 0.96 and outperformed all the state-of-the-art methods. On real-world logs, we identified all types of process drifts in a comprehensive manner. Finally, we conducted a user study highlighting that our visualizations are easy to use and useful as perceived by process mining experts. In this way, our work contributes to research on process mining, event sequence analysis, and visualization of temporal data." @default.
• W3118943338 created "2021-01-18" @default.
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• W3118943338 date "2022-08-01" @default.
• W3118943338 modified "2023-10-16" @default.
• W3118943338 title "Visual Drift Detection for Event Sequence Data of Business Processes" @default.
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• W3118943338 doi "https://doi.org/10.1109/tvcg.2021.3050071" @default.
• W3118943338 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33417557" @default.
• W3118943338 hasPublicationYear "2022" @default.
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