Exploiting recurrent graph neural networks for suffix prediction in predictive monitoring-Reference-Cited by-同舟云学术

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Exploiting recurrent graph neural networks for suffix prediction in predictive monitoring

Published:2024-07-09 Issue:9 Volume:106 Page:3085-3111
ISSN:0010-485X
Container-title:Computing
language:en
Short-container-title:Computing

Author:

Rama-Maneiro Efrén,Vidal Juan C.,Lama Manuel,Monteagudo-Lago Pablo

Funder

Consellería de Educación, Universidade e Formación Profesional

European Regional Development Fund

Ministerio de Ciencia e Innovación

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00607-024-01315-9.pdf

Reference43 articles.

1. Aalst WMP (2016) Process mining: data science in action, 2nd edn. Springer, Cham

2. Lecture Notes in Business Information Processing;CD Francescomarino,2022

3. Francescomarino CD, Dumas M, Maggi FM, Teinemaa I (2019) Clustering-based predictive process monitoring. IEEE Trans Serv Comput 12(6):896–909

4. Lee WLJ, Parra D, Munoz-Gama J, Sepúlveda M (2018) Predicting process behavior meets factorization machines. Expert Syst Appl 112:87–98

5. Leontjeva A, Conforti R, Francescomarino CD, Dumas M, Maggi FM (2015) Complex symbolic sequence encodings for predictive monitoring of business processes. In: 13th international conference on business process management (BPM 2015), vol 9253. pp 297–313

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