TY  - JOUR
A1  - Oberdorf, Felix
A1  - Schaschek, Myriam
A1  - Weinzierl, Sven
A1  - Stein, Nikolai
A1  - Matzner, Martin
A1  - Flath, Christoph M.
T1  - Predictive end-to-end enterprise process network monitoring
JF  - Business & Information Systems Engineering
N2  - 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.
KW  - predictive process analytics
KW  - predictive process monitoring
KW  - deep learning
KW  - machine learning
KW  - neural network
KW  - business process anagement
KW  - process mining
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323814
SN  - 2363-7005
VL  - 65
IS  - 1
ER  - 
TY  - JOUR
A1  - Leimeister, Jan Marco
A1  - Stieglitz, Stefan
A1  - Matzner, Martin
A1  - Kundisch, Dennis
A1  - Flath, Christoph
A1  - Röglinger, Maximilian
T1  - Quo Vadis Conferences in the Business and Information Systems Engineering (BISE) Community After Covid
BT  - What Can Stay, What Should Go, What Do We Need to Change for Our Future Scientific Conferences?
JF  - Business & Information Systems Engineering
KW  - IT in Business
KW  - business and management
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-308902
SN  - 2363-7005
SN  - 1867-0202
VL  - 63
IS  - 6
ER  -