Scientific paper resulting from the work of Renato Marinho Alves. Published at the 10th World Conference on Information Systems and Technologies, titled “Context-aware Completion Time Prediction For Business Process Monitoring” and presented by Renato in a online session. The paper features collaboration from researchers MSc. Luciana Barbieri, MSc Kleber Stroeh, Prof. Dr. Edmund Madeira (advisor) and Prof. Dr. Sarajane Marques Peres.
Abstract: Time-based prediction problems are often modeled using machine learning. In business process monitoring, we associate time-based prediction tasks with predictive process monitoring goals. Solutions for prediction are based on typical pieces of information recorded in an event log related to business processes, such as timestamps and execution of activities. However, relevant characteristics about the business process are left out when we select only these attributes. In this context, we state the use of process contextual data should provide relevant information to improve predictions. In this paper, we discuss the completion time prediction problem by manually selecting and adding contextual process instance attributes into the description of process instances before input them to prediction models implemented using LSTM neural network and Annotated Transition Systems. Our approach focuses on how an attribute influences the completion time of existing cases, and how they affect the prediction performance. We evaluated our approach using real-world event logs and compared them with baseline predictions. The results showed predictions models trained using contextual attributes performed better, improving prediction response by up to 83%.
Complete paper here (Springer Link).
Reference: Alves, R.M., Barbieri, L., Stroeh, K., Peres, S.M., Madeira, E.R.M. (2022). Context-Aware Completion Time Prediction for Business Process Monitoring. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-04819-7_35
Bibtex:
@InProceedings{10.1007/978-3-031-04819-7_35,
author=”Alves, Renato Marinho
and Barbieri, Luciana
and Stroeh, Kleber
and Peres, Sarajane Marques
and Madeira, Edmundo Roberto Mauro”,
editor=”Rocha, Alvaro
and Adeli, Hojjat
and Dzemyda, Gintautas
and Moreira, Fernando”,
title=”Context-Aware Completion Time Prediction for Business Process Monitoring”,
booktitle=”Information Systems and Technologies”,
year=”2022″,
publisher=”Springer International Publishing”,
address=”Cham”,
pages=”355–365″,
isbn=”978-3-031-04819-7″}
Artigo científico resultante do trabalho de Renato Marinho Alves. Publicado no 10th World Conference on Information Systems and Technologies, intitulado “Context-aware Completion Time Prediction For Business Process Monitoring” e apresentado por Renato em uma sessão online. O paper conta com a colaboração de MSc. Luciana Barbieri, MSc Kleber Stroeh, Prof. Dr. Edmund Madeira (orientador) and Profa. Dra. Sarajane Marques Peres.
Artigo completo aqui – em inglês (Springer Link).
Referência: Alves, R.M., Barbieri, L., Stroeh, K., Peres, S.M., Madeira, E.R.M. (2022). Context-Aware Completion Time Prediction for Business Process Monitoring. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F. (eds) Information Systems and Technologies. WorldCIST 2022. Lecture Notes in Networks and Systems, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-04819-7_35
Bibtex:
@InProceedings{10.1007/978-3-031-04819-7_35,
author=”Alves, Renato Marinho
and Barbieri, Luciana
and Stroeh, Kleber
and Peres, Sarajane Marques
and Madeira, Edmundo Roberto Mauro”,
editor=”Rocha, Alvaro
and Adeli, Hojjat
and Dzemyda, Gintautas
and Moreira, Fernando”,
title=”Context-Aware Completion Time Prediction for Business Process Monitoring”,
booktitle=”Information Systems and Technologies”,
year=”2022″,
publisher=”Springer International Publishing”,
address=”Cham”,
pages=”355–365″,
isbn=”978-3-031-04819-7″}
