Online Predicting Conformance of Business Process with Recurrent Neural Networks

Jiaojiao Wang, Dingguo Yu, Xiaoyu Ma, Chang Liu, Victor Chang, Xuewen Shen

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.

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Paper Citation


in Harvard Style

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 - Volume 1: IoTBDS, ISBN 978-989-758-426-8, pages 88-100. DOI: 10.5220/0009394400880100


in Bibtex Style

@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 - Volume 1: IoTBDS,},
year={2020},
pages={88-100},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009394400880100},
isbn={978-989-758-426-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Online Predicting Conformance of Business Process with Recurrent Neural Networks
SN - 978-989-758-426-8
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