TY - GEN
T1 - Attention mechanism in predictive business process monitoring
AU - Jalayer, Abdulrahman
AU - Kahani, Mohsen
AU - Beheshti, Amin
AU - Pourmasoumi, Asef
AU - Motahari-Nezhad, Hamid Reza
PY - 2020
Y1 - 2020
N2 - Business process monitoring techniques have been investigated in depth over the last decade to enable organizations to deliver process insight. Recently, a new stream of work in predictive business process monitoring leveraged deep learning techniques to unlock the potential business value locked in process execution event logs. These works use Recurrent Neural Networks, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and suffer from misinformation and accuracy as they use the last hidden state (as the context vector) for the purpose of predicting the next event. On the other hand, in operational processes, traces may be very long, which makes the above methods inappropriate for analyzing them. In addition, in predicting the next events in a running case, some of the previous events should be given a higher priority. To address these shortcomings, in this paper, we present a novel approach inspired by the notion of attention mechanism, utilized in Natural Language Processing and, particularly, in Neural Machine Translation. Our proposed approach uses all hidden states to accurately predict future behavior and the outcome of individual activities. Experimental evaluation of real-world event logs revealed that the use of attention mechanisms in the proposed approach leads to a more accurate prediction.
AB - Business process monitoring techniques have been investigated in depth over the last decade to enable organizations to deliver process insight. Recently, a new stream of work in predictive business process monitoring leveraged deep learning techniques to unlock the potential business value locked in process execution event logs. These works use Recurrent Neural Networks, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and suffer from misinformation and accuracy as they use the last hidden state (as the context vector) for the purpose of predicting the next event. On the other hand, in operational processes, traces may be very long, which makes the above methods inappropriate for analyzing them. In addition, in predicting the next events in a running case, some of the previous events should be given a higher priority. To address these shortcomings, in this paper, we present a novel approach inspired by the notion of attention mechanism, utilized in Natural Language Processing and, particularly, in Neural Machine Translation. Our proposed approach uses all hidden states to accurately predict future behavior and the outcome of individual activities. Experimental evaluation of real-world event logs revealed that the use of attention mechanisms in the proposed approach leads to a more accurate prediction.
KW - Attention Mechanism
KW - Business Process Management
KW - Deep Learning
KW - LSTM
KW - Predictive Process Monitoring
KW - Process Mining
KW - Seq2Seq
UR - http://www.scopus.com/inward/record.url?scp=85096599419&partnerID=8YFLogxK
U2 - 10.1109/EDOC49727.2020.00030
DO - 10.1109/EDOC49727.2020.00030
M3 - Conference proceeding contribution
AN - SCOPUS:85096599419
T3 - IEEE International Enterprise Distributed Object Computing Conference-EDOC
SP - 181
EP - 186
BT - Proceedings - 2020 IEEE 24th International Enterprise Distributed Object Computing Conference, EDOC 2020
PB - Institute of Electrical and Electronics Engineers (IEEE)
CY - Piscataway, NJ
T2 - 24th IEEE International Enterprise Distributed Object Computing Conference, EDOC 2020
Y2 - 5 October 2020 through 8 October 2020
ER -