Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning
The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are diff...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/15/6931 |
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| author | Alexandros Bousdekis Athanasios Kerasiotis Silvester Kotsias Georgia Theodoropoulou Georgios Miaoulis Djamchid Ghazanfarpour |
| author_facet | Alexandros Bousdekis Athanasios Kerasiotis Silvester Kotsias Georgia Theodoropoulou Georgios Miaoulis Djamchid Ghazanfarpour |
| author_sort | Alexandros Bousdekis |
| collection | DOAJ |
| description | The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called “spaghetti-like” process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case. |
| first_indexed | 2024-03-11T00:16:23Z |
| format | Article |
| id | doaj.art-51649d2ccbc64d828aa4263f07783030 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:16:23Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-51649d2ccbc64d828aa4263f077830302023-11-18T23:36:24ZengMDPI AGSensors1424-82202023-08-012315693110.3390/s23156931Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement LearningAlexandros Bousdekis0Athanasios Kerasiotis1Silvester Kotsias2Georgia Theodoropoulou3Georgios Miaoulis4Djamchid Ghazanfarpour5Department of Informatics and Computer Engineering, University of West Attica, 12242 Egaleo, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, 12242 Egaleo, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, 12242 Egaleo, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, 12242 Egaleo, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, 12242 Egaleo, GreeceDepartment of Informatics, University of Limoges, 87032 Limoges, FranceThe analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called “spaghetti-like” process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case.https://www.mdpi.com/1424-8220/23/15/6931predictive business process monitoringprocess miningbusiness process managementmachine learningdata analytics |
| spellingShingle | Alexandros Bousdekis Athanasios Kerasiotis Silvester Kotsias Georgia Theodoropoulou Georgios Miaoulis Djamchid Ghazanfarpour Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning Sensors predictive business process monitoring process mining business process management machine learning data analytics |
| title | Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning |
| title_full | Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning |
| title_fullStr | Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning |
| title_full_unstemmed | Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning |
| title_short | Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning |
| title_sort | modelling and predictive monitoring of business processes under uncertainty with reinforcement learning |
| topic | predictive business process monitoring process mining business process management machine learning data analytics |
| url | https://www.mdpi.com/1424-8220/23/15/6931 |
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