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dc.contributor.authorYeshchenko, A
dc.contributor.authorDi Ciccio, C
dc.contributor.authorMendling, J
dc.contributor.authorPolyvyanyy, A
dc.date.accessioned2021-02-11T22:05:11Z
dc.date.available2021-02-11T22:05:11Z
dc.date.issued2021-01-08
dc.identifier.citationYeshchenko, 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.
dc.identifier.issn1077-2626
dc.identifier.urihttp://hdl.handle.net/11343/260527
dc.description.abstractEvent 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.
dc.languageeng
dc.publisherInstitute of Electrical and Electronics Engineers
dc.titleVisual Drift Detection for Sequence Data Analysis of Business Processes.
dc.typeJournal Article
dc.identifier.doi10.1109/TVCG.2021.3050071
melbourne.affiliation.departmentComputing and Information Systems
melbourne.source.titleIEEE Transactions on Visualization and Computer Graphics
melbourne.source.volumePP
melbourne.source.issue99
melbourne.source.pages1-1
melbourne.identifier.arcDP180102839
melbourne.elementsid1493565
melbourne.internal.embargodate2023-01-08
melbourne.contributor.authorPolyvyanyy, Artem
dc.identifier.eissn1941-0506
melbourne.identifier.fundernameidAustralian Research Council, DP180102839
melbourne.accessrightsThis item is embargoed and will be available on 2023-01-08


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