Abstract
Conformance Checking is a problem to detect and describe the differences between a given process model representing the expected behaviour of a business process and an event log recording its actual execution by the Process-aware Information System (PAIS). However, such existing conformance checking techniques are offline and mainly applied for the completely executed process instances, which cannot provide the real-time conformance-oriented process monitoring for an on-going process instance. Therefore, in this paper, we propose three approaches for online conformance prediction by constructing a classification model automatically based on the historical event log and the existing reference process model. By utilizing Recurrent Neural Networks, these approaches can capture the features that have a decisive effect on the conformance for an executed case to build a prediction model and then use this model to predict the conformance of a running case. The experimental results on two real datasets show that our approaches outperform the state-of-the-art ones in terms of prediction accuracy and time performance.
Original language | English |
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Title of host publication | IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security |
Editors | Gary Wills, Peter Kacsuk, Victor Chang |
Publisher | SciTePress |
Pages | 88-100 |
Number of pages | 13 |
ISBN (Electronic) | 9789897584268 |
DOIs | |
Publication status | Published - 25 Apr 2020 |
Event | 5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020 - Virtual, Online Duration: 7 May 2020 → 9 May 2020 |
Publication series
Name | IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security |
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Conference
Conference | 5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020 |
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City | Virtual, Online |
Period | 7/05/20 → 9/05/20 |
Bibliographical note
Copyrightc 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Funding Information:
This work was supported by the Key Research and Development Program of Zhejiang Province, China (Grant No.2019C03138). Dingguo Yu is the corresponding author ([email protected]).
Keywords
- Classifier
- Online Conformance Checking
- Predictive Business Process Monitoring
- Recurrent Neural Networks