Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/311818 
Year of Publication: 
2022
Citation: 
[Journal:] Business & Information Systems Engineering [ISSN:] 1867-0202 [Volume:] 65 [Issue:] 1 [Publisher:] Springer Fachmedien Wiesbaden GmbH [Place:] Wiesbaden [Year:] 2022 [Pages:] 49-64
Publisher: 
Springer Fachmedien Wiesbaden GmbH, Wiesbaden
Abstract: 
Ever-growing data availability combined with rapid progress in analytics has laid the foundation for the emergence of business process analytics. Organizations strive to leverage predictive process analytics to obtain insights. However, current implementations are designed to deal with homogeneous data. Consequently, there is limited practical use in an organization with heterogeneous data sources. The paper proposes a method for predictive end-to-end enterprise process network monitoring leveraging multi-headed deep neural networks to overcome this limitation. A case study performed with a medium-sized German manufacturing company highlights the method's utility for organizations.
Subjects: 
Predictive process analytics
Predictive process monitoring
Deep learning
Machine learning
Neural network
Business process anagement
Process mining
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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