Predição de tempo restante para conclusão de processos de negócio utilizando aprendizado profundo
Resumo
A predição eficiente do tempo restante para conclusão de uma instância de processo contribui na prevenção de esperas incertas, descoberta de gargalos em processos e assistência em sistemas de alerta. Este trabalho utiliza arquiteturas de aprendizado profundo baseadas em redes recorrentes para predizer o tempo restante para conclusão de um processo de negócio, que superam soluções do estado da arte. As arquiteturas utilizadas são validadas com dois conjuntos de dados públicos, facilitando a reprodutibilidade dos experimentos.
Palavras-chave:
Predição de tempo de conclusão, Processos de Negócio, Aprendizado profundo
Referências
Bukhsh, Z. A., Saeed, A., and Dijkman, R. M. (2021). Processtransformer: Predictive business process monitoring with transformer network. arXiv preprint arXiv:2104.00721.
Castro, M. A., Souza Jr, N., Escovedo, T., Lopes, H., and Kalinowski, M. (2022). Mineração de processos aplicada à auditoria interna na marinha do brasil. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados, pages 241–253. SBC.
Kalenkova, A., Ageev, A., Lomazova, I. A., and van der Aalst, W. M. (2017). E-government services: Comparing real and expected user behavior. In International Conference on Business Process Management, pages 484–496. Springer.
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).
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.
Navarin, N., Vincenzi, B., Polato, M., and Sperduti, A. (2017). Lstm networks for data-aware remaining time prediction of business process instances. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–7. IEEE.
Park, G. and Song, M. (2020). Predicting performances in business processes using deep neural networks. Decision Support Systems, 129:113191.
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.
Polato, M. (2017). Dataset belonging to the help desk log of an italian company.
Ponsard, C. and Darimont, R. (2019). Towards goal-oriented analysis and redesign of bpmn models. In MODELSWARD, pages 527–533.
Reijers, H. A. (2021). Business process management: The evolution of a discipline. Computers in Industry, 126:103404.
Stjepić, A.-M., Ivančić, 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.
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.
van Dongen, B. (2012). Bpi challenge 2012.
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.
Venugopal, I., Töllich, 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.
Castro, M. A., Souza Jr, N., Escovedo, T., Lopes, H., and Kalinowski, M. (2022). Mineração de processos aplicada à auditoria interna na marinha do brasil. In Anais do XXXVII Simpósio Brasileiro de Bancos de Dados, pages 241–253. SBC.
Kalenkova, A., Ageev, A., Lomazova, I. A., and van der Aalst, W. M. (2017). E-government services: Comparing real and expected user behavior. In International Conference on Business Process Management, pages 484–496. Springer.
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).
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.
Navarin, N., Vincenzi, B., Polato, M., and Sperduti, A. (2017). Lstm networks for data-aware remaining time prediction of business process instances. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–7. IEEE.
Park, G. and Song, M. (2020). Predicting performances in business processes using deep neural networks. Decision Support Systems, 129:113191.
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.
Polato, M. (2017). Dataset belonging to the help desk log of an italian company.
Ponsard, C. and Darimont, R. (2019). Towards goal-oriented analysis and redesign of bpmn models. In MODELSWARD, pages 527–533.
Reijers, H. A. (2021). Business process management: The evolution of a discipline. Computers in Industry, 126:103404.
Stjepić, A.-M., Ivančić, 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.
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.
van Dongen, B. (2012). Bpi challenge 2012.
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.
Venugopal, I., Töllich, 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.
Publicado
25/09/2023
Como Citar
SILVA, Ronildo Oliveira da; MAGALHÃES, Regis Pires; CRUZ, Lívia Almada; DE SOUZA, Criston Pereira; VASCONCELOS, Davi Romero de; MACÊDO, José Antônio Fernandes de.
Predição de tempo restante para conclusão de processos de negócio utilizando aprendizado profundo. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 38. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 141-153.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2023.231707.
