PGraphD*: Methods for Drift Detection and Localisation Using Deep Learning Modelling of Business Processes

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...

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Detalles Bibliográficos
Autores principales: Hanga, Khadijah Muzzammil, Kovalchuk, Yevgeniya, Gaber, Mohamed Medhat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324690/
https://www.ncbi.nlm.nih.gov/pubmed/35885132
http://dx.doi.org/10.3390/e24070910

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9324690/
https://www.ncbi.nlm.nih.gov/pubmed/35885132
http://dx.doi.org/10.3390/e24070910

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