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.
|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|
|Number of pages||13|
|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
|Name||IoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security|
|Conference||5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020|
|Period||7/05/20 → 9/05/20|
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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@example.com).
- Online Conformance Checking
- Predictive Business Process Monitoring
- Recurrent Neural Networks