Attention Mechanism in Predictive Business Process Monitoring

2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC) ◽

10.1109/edoc49727.2020.00030 ◽

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

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HAM-Net: Predictive Business Process Monitoring with a hierarchical attention mechanism

Knowledge-Based Systems ◽

10.1016/j.knosys.2021.107722 ◽

2021 ◽

pp. 107722

Author(s):

Abdulrahman Jalayer ◽

Mohsen Kahani ◽

Asef Pourmasoumi ◽

Amin Beheshti

Keyword(s):

Process Monitoring ◽

Business Process ◽

Attention Mechanism ◽

Business Process Monitoring

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Creating a Business Process Monitoring System“A-IOMS” for Software Development

Chinese Business Review ◽

10.17265/1537-1506/2012.06.009 ◽

2012 ◽

Vol 11 (06) ◽

Keyword(s):

Software Development ◽

Monitoring System ◽

Process Monitoring ◽

Business Process ◽

System A ◽

Business Process Monitoring

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Secure communication protocol for real-time business process monitoring

International Journal of Internet Technology and Secured Transactions ◽

10.1504/ijitst.2014.065183 ◽

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

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Towards the Enhancement of Business Process Monitoring for Complex Logistics Chains

Business Process Management Workshops - Lecture Notes in Business Information Processing ◽

10.1007/978-3-319-06257-0_24 ◽

2014 ◽

pp. 305-317 ◽

Cited By ~ 10

Author(s):

Cristina Cabanillas ◽

Anne Baumgrass ◽

Jan Mendling ◽

Patricia Rogetzer ◽

Bruno Bellovoda

Keyword(s):

Process Monitoring ◽

Business Process ◽

Business Process Monitoring

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Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction

Expert Systems with Applications ◽

10.1016/j.eswa.2011.12.007 ◽

2012 ◽

Vol 39 (5) ◽

pp. 6061-6068 ◽

Cited By ~ 39

Author(s):

Bokyoung Kang ◽

Dongsoo Kim ◽

Suk-Ho Kang

Keyword(s):

Real Time ◽

Process Monitoring ◽

Business Process ◽

Monitoring Method ◽

Business Process Monitoring ◽

Abnormal Termination

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Business process monitoring using web services in B2B e-commerce

Proceedings 16th International Parallel and Distributed Processing Symposium ◽

10.1109/ipdps.2002.1016632 ◽

2002 ◽

Cited By ~ 11

Author(s):

C. McGregor ◽

S. Kumaran

Keyword(s):

Web Services ◽

Process Monitoring ◽

Business Process ◽

Business Process Monitoring

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Explaining the Incorrect Temporal Events during Business Process Monitoring by Means of Compliance Rules and Model-Based Diagnosis

2013 17th IEEE International Enterprise Distributed Object Computing Conference Workshops ◽

10.1109/edocw.2013.25 ◽

2013 ◽

Cited By ~ 9

Author(s):

Maria Teresa Gomez-Lopez ◽

Rafael M. Gasca ◽

Stefanie Rinderle-Ma

Keyword(s):

Process Monitoring ◽

Business Process ◽

Model Based ◽

Business Process Monitoring ◽

Temporal Events

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Model-Driven Event Query Generation for Business Process Monitoring

Lecture Notes in Computer Science - Service-Oriented Computing – ICSOC 2013 Workshops ◽

10.1007/978-3-319-06859-6_36 ◽

2014 ◽

pp. 406-418 ◽

Cited By ~ 4

Author(s):

Michael Backmann ◽

Anne Baumgrass ◽

Nico Herzberg ◽

Andreas Meyer ◽

Mathias Weske

Keyword(s):

Process Monitoring ◽

Business Process ◽

Model Driven ◽

Query Generation ◽

Business Process Monitoring

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Prescriptive Business Process Monitoring for Recommending Next Best Actions

Lecture Notes in Business Information Processing - Business Process Management Forum ◽

10.1007/978-3-030-58638-6_12 ◽

2020 ◽

pp. 193-209

Author(s):

Sven Weinzierl ◽

Sebastian Dunzer ◽

Sandra Zilker ◽

Martin Matzner

Keyword(s):

Process Monitoring ◽

Business Process ◽

Business Process Monitoring

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Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

Business & Information Systems Engineering ◽

10.1007/s12599-020-00645-0 ◽

2020 ◽

Author(s):

Wolfgang Kratsch ◽

Jonas Manderscheid ◽

Maximilian Röglinger ◽

Johannes Seyfried

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Process Monitoring ◽

Business Process ◽

Outcome Prediction ◽

Business Process Monitoring

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