Visual Drift Detection for Sequence Data Analysis of Business Processes.
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Author
Yeshchenko, A; Di Ciccio, C; Mendling, J; Polyvyanyy, ADate
2021-01-08Source Title
IEEE Transactions on Visualization and Computer GraphicsPublisher
Institute of Electrical and Electronics EngineersUniversity of Melbourne Author/s
Polyvyanyy, ArtemAffiliation
Computing and Information SystemsMetadata
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Journal ArticleCitations
Yeshchenko, A., Di Ciccio, C., Mendling, J. & Polyvyanyy, A. (2021). Visual Drift Detection for Sequence Data Analysis of Business Processes.. IEEE Transactions on Visualization and Computer Graphics, PP (99), pp.1-1. https://doi.org/10.1109/TVCG.2021.3050071.Access Status
This item is embargoed and will be available on 2023-01-08ARC Grant code
ARC/DP180102839Abstract
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 paper, 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.
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