PGraphD*: methods for drift detection and localisation using deep learning modelling of business processes

Hanga, K. M., Kovalchuk, Y. ORCID: https://orcid.org/0000-0003-4306-4680 and Gaber, M. M. (2022) PGraphD*: methods for drift detection and localisation using deep learning modelling of business processes. Entropy, 24 (7). 910. ISSN 1099-4300 doi: 10.3390/e24070910

Abstract/Summary

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
URI	https://centaur.reading.ac.uk/id/eprint/106022
Identification Number/DOI	10.3390/e24070910
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	MDPI Publishing
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