Online predicting conformance of business process with recurrent neural networks


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.

Publication DOI:
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: Copyright c 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 (
Event Title: 5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020
Event Type: Other
Event Dates: 2020-05-07 - 2020-05-09
Uncontrolled Keywords: Classifier,Online Conformance Checking,Predictive Business Process Monitoring,Recurrent Neural Networks,Software,Computer Networks and Communications
ISBN: 9789897584268
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http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Conference contribution
Published Date: 2020-04-25
Authors: Wang, Jiaojiao
Yu, Dingguo
Ma, Xiaoyu
Liu, Chang
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Shen, Xuewen

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