Predictive End-to-End Enterprise Process Network Monitoring
| dc.contributor.author | Oberdorf, Felix | |
| dc.contributor.author | Schaschek, Myriam | |
| dc.contributor.author | Weinzierl, Sven | |
| dc.contributor.author | Stein, Nikolai | |
| dc.contributor.author | Matzner, Martin | |
| dc.contributor.author | Flath, Christoph M. | |
| dc.date.accessioned | 2023-02-27T10:50:55Z | |
| dc.date.available | 2023-02-27T10:50:55Z | |
| dc.date.issued | 2023 | |
| dc.description.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. | de |
| dc.identifier.doi | 10.1007/s12599-022-00778-4 | |
| dc.identifier.pissn | 1867-0202 | |
| dc.identifier.uri | http://dx.doi.org/10.1007/s12599-022-00778-4 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/40414 | |
| dc.publisher | Springer | |
| dc.relation.ispartof | Business & Information Systems Engineering: Vol. 65, No. 1 | |
| dc.relation.ispartofseries | Business & Information Systems Engineering | |
| dc.subject | Business process anagement||Deep learning||Machine learning||Neural network||Predictive process analytics||Predictive process monitoring||Process mining | |
| dc.title | Predictive End-to-End Enterprise Process Network Monitoring | de |
| dc.type | Text/Journal Article | |
| gi.citation.endPage | 64 | |
| gi.citation.startPage | 49 |