Authors:
Jiaojiao Wang
1
;
Dingguo Yu
1
;
Xiaoyu Ma
1
;
Chang Liu
1
;
Victor Chang
2
and
Xuewen Shen
3
Affiliations:
1
Institute of Intelligent Media Technology, Communication University of Zhejiang, Hangzhou, China, Key Lab of Film and TV Media Technology of Zhejiang Province, Hangzhou, China
;
2
School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, U.K
;
3
School of Media Engineering, Communication University of Zhejiang, Hangzhou, China
Keyword(s):
Online Conformance Checking, Recurrent Neural Networks, Predictive Business Process Monitoring, Classifier.
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 re
al datasets show that our approaches outperform the state-of-the-art ones in terms of prediction accuracy and time performance.
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