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English Information

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Authors
# Name
1 Ronildo Silva(ronildo.oliveira@alu.ufc.br)
2 Regis Magalhães(regismagalhaes@ufc.br)
3 Lívia da Silva(livia_de_azevedo@yahoo.com.br)
4 Criston de Souza(criston@ufc.br)
5 Davi Romero( daviromero@insight.ufc.br)
6 José Macedo(jose.macedo@dc.ufc.br )

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Reference
# Reference
1 Bukhsh, Z. A., Saeed, A., and Dijkman, R. M. (2021). Processtransformer: Predictive business process monitoring with transformer network. arXiv preprint arXiv:2104.00721.
2 Castro, M. A., Souza Jr, N., Escovedo, T., Lopes, H., and Kalinowski, M. (2022). Minerac¸ao de processos aplicada ˜ a auditoria interna na marinha do brasil. In ` Anais do XXXVII Simposio Brasileiro de Bancos de Dados ´ , pages 241–253. SBC.
3 Kalenkova, A., Ageev, A., Lomazova, I. A., and van der Aalst, W. M. (2017). Egovernment services: Comparing real and expected user behavior. In International Conference on Business Process Management, pages 484–496. Springer.
4 Mello, P., Santoro, F., and Revoredo, K. (2020). It incident solving domain experiment on business process failure prediction. Journal of Information and Data Management, 11(1).
5 Mello, P. O., Revoredo, K., and Santoro, F. (2019). Business process failure prediction: a case study. In Anais do VII Symposium on Knowledge Discovery, Mining and Learning, pages 89–96. SBC.
6 Navarin, N., Vincenzi, B., Polato, M., and Sperduti, A. (2017). Lstm networks for dataaware remaining time prediction of business process instances. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–7. IEEE.
7 Park, G. and Song, M. (2020). Predicting performances in business processes using deep neural networks. Decision Support Systems, 129:113191.
8 Paschek, D., Luminosu, C. T., and Draghici, A. (2017). Automated business process management–in times of digital transformation using machine learning or artificial intelligence. In MATEC Web of Conferences, volume 121, page 04007. EDP Sciences.
9 Polato, M. (2017). Dataset belonging to the help desk log of an italian company.
10 Ponsard, C. and Darimont, R. (2019). Towards goal-oriented analysis and redesign of bpmn models. In MODELSWARD, pages 527–533.
11 Reijers, H. A. (2021). Business process management: The evolution of a discipline. Computers in Industry, 126:103404.
12 Stjepic, A.-M., Ivan ´ ciˇ c, L., and Vugec, D. S. (2020). Mastering digital transformation ´ through business process management: Investigating alignments, goals, orchestration, and roles. Journal of entrepreneurship, management and innovation, 16(1):41–74.
13 Tax, N., Verenich, I., Rosa, M. L., and Dumas, M. (2017). Predictive business process monitoring with lstm neural networks. In International Conference on Advanced Information Systems Engineering, pages 477–492. Springer.
14 van Dongen, B. (2012). Bpi challenge 2012.
15 Venkateswaran, P., Muthusamy, V., Isahagian, V., and Venkatasubramanian, N. (2021). Robust and generalizable predictive models for business processes. In Business Process Management: 19th International Conference, BPM 2021, Rome, Italy, September 06–10, 2021, Proceedings, pages 105–122. Springer.
16 Venugopal, I., Tollich, J., Fairbank, M., and Scherp, A. (2021). A comparison of deep- ¨ learning methods for analysing and predicting business processes. In 2021 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.