Online predicting conformance of business process with recurrent neural networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
13 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationIoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security
EditorsGary Wills, Peter Kacsuk, Victor Chang
PublisherSciTePress
Pages88-100
Number of pages13
ISBN (Electronic)9789897584268
Publication statusPublished - 7 May 2020
Event5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020 - Virtual, Online
Duration: 7 May 20209 May 2020

Publication series

NameIoTBDS 2020 - Proceedings of the 5th International Conference on Internet of Things, Big Data and Security

Conference

Conference5th International Conference on Internet of Things, Big Data and Security, IoTBDS 2020
CityVirtual, Online
Period7/05/209/05/20

Bibliographical note

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 (yudg@cuz.edu.cn).

Publisher Copyright:
Copyright © 2020 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved.

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