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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. (More)

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Paper citation in several formats:
Wang, J.; Yu, D.; Ma, X.; Liu, C.; Chang, V. and Shen, X. (2020). Online Predicting Conformance of Business Process with Recurrent Neural Networks. In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS; ISBN 978-989-758-426-8; ISSN 2184-4976, SciTePress, pages 88-100. DOI: 10.5220/0009394400880100

@conference{iotbds20,
author={Jiaojiao Wang. and Dingguo Yu. and Xiaoyu Ma. and Chang Liu. and Victor Chang. and Xuewen Shen.},
title={Online Predicting Conformance of Business Process with Recurrent Neural Networks},
booktitle={Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS},
year={2020},
pages={88-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009394400880100},
isbn={978-989-758-426-8},
issn={2184-4976},
}

TY - CONF

JO - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - IoTBDS
TI - Online Predicting Conformance of Business Process with Recurrent Neural Networks
SN - 978-989-758-426-8
IS - 2184-4976
AU - Wang, J.
AU - Yu, D.
AU - Ma, X.
AU - Liu, C.
AU - Chang, V.
AU - Shen, X.
PY - 2020
SP - 88
EP - 100
DO - 10.5220/0009394400880100
PB - SciTePress