Hanga, Khadijah Muzzammil;
Kovalchuk, Yevgeniya;
Gaber, Mohamed Medhat;
(2022)
PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes.
Entropy
, 24
(7)
, Article 910. 10.3390/e24070910.
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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.
| Type: | Article |
|---|---|
| Title: | PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes |
| Location: | Switzerland |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/e24070910 |
| Publisher version: | https://doi.org/10.3390/e24070910 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third-party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| Keywords: | Business process management, concept drift detection, concept drift localisation, deep learning, graph streams, long short-term memory, process mining |
| UCL classification: | UCL |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10176839 |
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