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
Identification Number:	10.3390/e24070910
Dates:	Date Event 27 June 2022 Accepted 30 June 2022 Published Online
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:	Architecture, Built Environment, Computing and Engineering > Computer Science
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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