Text-Aware Predictive Monitoring of Business Processes
Abstract: The real-time prediction of business processes using historical event data is an important capability of modern business process monitoring systems. Existing process prediction methods are able to also exploit the data perspective of recorded events, in addition to the control-flow perspective. However, while well-structured numerical or categorical attributes are considered in many prediction techniques, almost no technique is able to utilize text documents written in natural language, which can hold information critical to the prediction task. In this paper, we illustrate the design, implementation, and evaluation of a novel text-aware process prediction model based on Long Short-Term Memory (LSTM) neural networks and natural language models. The proposed model can take categorical, numerical and textual attributes in event data into account to predict the activity and timestamp of the next event, the outcome, and the cycle time of a running process instance. Experiments show tha.... https://www.tib-op.org/ojs/index.php/bis/article/view/62
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Text-Aware Predictive Monitoring of Business Processes ; volume:1 ; day:02 ; month:07 ; year:2021
Business information systems ; 1 (02.07.2021)
- Creator
- DOI
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10.52825/bis.v1i.62
- URN
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urn:nbn:de:101:1-2021090812540633868757
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:19 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Pegoraro, Marco
- Uysal, Merih Seran
- Georgi, David Benedikt
- Aalst, Wil van der
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