PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes
Hanga, Khadijah and Kovalchuk, Yevgeniya and Gaber, Mohamed Medhat (2022) PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes. Entropy, 24 (7). ISSN 1099-4300
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Abstract
This paper presents a set of methods, jointly called PGraphD*, which includes two new methods (PGraphDD-QM and PGraphDD-SS) for drift detection and one new method (PGraphDL) for drift localisation in business processes. The methods are based on deep learning and graphs, with PGraphDD-QM and PGraphDD-SS employing a quality metric and a similarity score for detecting drifts, respectively. According to experimental results, PGraphDD-SS outperforms PGraphDD-QM in drift detection, achieving an accuracy score of 100% over the majority of synthetic logs and an accuracy score of 80% over a complex real-life log. Furthermore, PGraphDD-SS detects drifts with delays that are 59% shorter on average compared to the best performing state-of-the-art method.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.3390/e24070910 | ||||||
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| Uncontrolled Keywords: | process mining, business process management, graph streams, concept drift detection, concept drift localisation, deep learning, long short-term memory | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Mohamed Gaber | ||||||
| Date Deposited: | 12 Dec 2022 15:17 | ||||||
| Last Modified: | 12 Dec 2022 15:17 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/14005 |
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