Visual Drift Detection for Event Sequence Data of Business Processes

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 au...

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Vydáno v:IEEE transactions on visualization and computer graphics Ročník 28; číslo 8; s. 3050 - 3068
Hlavní autoři: Yeshchenko, Anton, Di Ciccio, Claudio, Mendling, Jan, Polyvyanyy, Artem
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Antibiotics
Business
Business process engineering
Business process management
Data mining
Data visualization
declarative process models
Domains
Drift
Erbium
Flow charts
Guidelines
process drifts
process mining
Sequence data
Software engineering
temporal data
Visualization
ISSN:1077-2626, 1941-0506, 1941-0506
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Shrnutí: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.
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ISSN:1077-2626
1941-0506
1941-0506
DOI:10.1109/TVCG.2021.3050071