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.

Visual Drift Detection for Sequence Data Analysis of Business Processes / Yeshchenko, A.; Di Ciccio, C.; Mendling, J.; Polyvyanyy, A.. - In: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS. - ISSN 1077-2626. - PP:(2021), pp. 1-1. [10.1109/TVCG.2021.3050071]

Visual Drift Detection for Sequence Data Analysis of Business Processes

Di Ciccio C.;
2021

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 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.
2021
Antibiotics; Business; Data mining; Data visualization; Declarative process models; Erbium; Guidelines; Process drifts; Process mining; Sequence data; Temporal data; Visualization
01 Pubblicazione su rivista::01a Articolo in rivista
Visual Drift Detection for Sequence Data Analysis of Business Processes / Yeshchenko, A.; Di Ciccio, C.; Mendling, J.; Polyvyanyy, A.. - In: IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS. - ISSN 1077-2626. - PP:(2021), pp. 1-1. [10.1109/TVCG.2021.3050071]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1522683
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