scholarly journals Predictive Business Process Monitoring – Remaining Time Prediction using Deep Neural Network with Entity Embedding

2019 ◽  
Vol 161 ◽  
pp. 1080-1088
Author(s):  
Nur Ahmad Wahid ◽  
Taufik Nur Adi ◽  
Hyerim Bae ◽  
Yulim Choi
Keyword(s):  
Neural Network ◽  
Process Monitoring ◽  
Business Process ◽  
Deep Neural Network ◽  
Time Prediction ◽  
Business Process Monitoring ◽  
Remaining Time

scholarly journals Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring

2019 ◽  
Vol 10 (4) ◽  
pp. 1-34 ◽  
Author(s):  
Ilya Verenich ◽  
Marlon Dumas ◽  
Marcello La Rosa ◽  
Fabrizio Maria Maggi ◽  
Irene Teinemaa
Keyword(s):  
Process Monitoring ◽  
Business Process ◽  
Prediction Methods ◽  
Time Prediction ◽  
Business Process Monitoring ◽  
Remaining Time

Attention Mechanism in Predictive Business Process Monitoring

2020 ◽  
Author(s):  
Abdulrahman Jalayer ◽  
Mohsen Kahani ◽  
Amin Beheshti ◽  
Asef Pourmasoumi ◽  
Hamid Reza Motahari-Nezhad
Keyword(s):  
Process Monitoring ◽  
Business Process ◽  
Attention Mechanism ◽  
Business Process Monitoring

scholarly journals Turning Process Monitoring with Deep Neural Network Trained by FEM Simulation

Procedia CIRP ◽  
2021 ◽  
Vol 104 ◽  
pp. 376-380
Author(s):  
Takashi Misaka ◽  
Jonny Herwan ◽  
Ichiro Ogura ◽  
Yoshiyuki Furukawa
Keyword(s):  
Neural Network ◽  
Process Monitoring ◽  
Deep Neural Network ◽  
Fem Simulation ◽  
Turning Process

Secure communication protocol for real-time business process monitoring

2014 ◽  
Vol 5 (3) ◽  
pp. 223
Author(s):  
Abdelrahman AlMahmoud ◽  
Maurizio Colombo ◽  
Chan Yeob Yeun ◽  
Hassan Al Muhairi
Keyword(s):  
Real Time ◽  
Process Monitoring ◽  
Business Process ◽  
Secure Communication ◽  
Communication Protocol ◽  
Business Process Monitoring

Towards the Enhancement of Business Process Monitoring for Complex Logistics Chains

2014 ◽  
pp. 305-317 ◽  
Author(s):  
Cristina Cabanillas ◽  
Anne Baumgrass ◽  
Jan Mendling ◽  
Patricia Rogetzer ◽  
Bruno Bellovoda
Keyword(s):  
Process Monitoring ◽  
Business Process ◽  
Business Process Monitoring

scholarly journals The Next Failure Time Prediction of Escalators via Deep Neural Network with Dynamic Time Warping Preprocessing

2020 ◽  
Vol 10 (16) ◽  
pp. 5622
Author(s):  
Zitong Zhou ◽  
Yanyang Zi ◽  
Jingsong Xie ◽  
Jinglong Chen ◽  
Tong An
Keyword(s):  
Neural Network ◽  
Dynamic Time Warping ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Failure Time ◽  
Low Complexity ◽  
Time Warping ◽  
Average Prediction Error ◽  
Time Prediction ◽  
Dynamic Time

The escalator is one of the most popular travel methods in public places, and the safe working of the escalator is significant. Accurately predicting the escalator failure time can provide scientific guidance for maintenance to avoid accidents. However, failure data have features of short length, non-uniform sampling, and random interference, which makes the data modeling difficult. Therefore, a strategy that combines data quality enhancement with deep neural networks is proposed for escalator failure time prediction in this paper. First, a comprehensive selection indicator (CSI) that can describe the stationarity and complexity of time series is established to select inherently excellent failure sequences. According to the CSI, failure sequences with high stationarity and low complexity are selected as the referenced sequences to enhance the quality of other failure sequences by using dynamic time warping preprocessing. Secondly, a deep neural network combining the advantages of a convolutional neural network and long short-term memory is built to train and predict quality-enhanced failure sequences. Finally, the failure-recall record of six escalators used for 6 years is analyzed by using the proposed method as a case study, and the results show that the proposed method can reduce the average prediction error of failure time to less than one month.

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