Conformance-oriented Predictive Process Monitoring in BPaaS Based on Combination of Neural Networks

Abstract

As a new cloud service for delivering complex business applications, Business Process as a Service (BPaaS) is another challenge faced by cloud service platforms recently. To effectively reduce the security risk caused by business process execution load in BPaaS, it is necessary to detect the non-compliant process executions (instances) from tenants in advance by checking and monitoring the conformance of the executing process instances in real-time. However, the vast majority of existing conformance checking techniques can only be applied to the process instances that have been executed completely offline and only focus on the conformance from the single control-flow perspective. We develop an extensible multi-perspective conformance measurement method to address these issues first and then investigate the predictive conformance monitoring approach by automatically constructing an online multi-perspective conformance prediction model based on deep learning techniques. In addition, to capture more decisive features in the model from both local information and long-distance dependency within an executed process instance, we propose an approach, called CNN-BiGRU, by combining Convolutional Neural Network (CNN) with a variant and enhancement of Recurrent Neural Network (RNN). Extensive experiments on two data sets demonstrate the effectiveness and efficiency of the proposed CNN-BiGRU.

Publication DOI: https://doi.org/10.1007/s10723-022-09613-2
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Additional Information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Funding: This work is supported by the Natural Science Foundation of China (No. 62002316), the VC Research (VCR 000067) for Prof. Chang, the Key Research and Development Program of Zhejiang Province (No. 2019C03138), the Key Science and Technology Project of Zhejiang Province (No. 2017C01010), and Zhejiang Provincial Natural Science Foundation (No. LQ20F020017).
Uncontrolled Keywords: Cloud security,Conformance-oriented predictive process monitoring,Convolution neural networks,Gated recurrent unit,Multi-perspective conformance,Software,Information Systems,Hardware and Architecture,Computer Networks and Communications
Publication ISSN: 1570-7873
Last Modified: 22 May 2024 07:21
Date Deposited: 01 Aug 2022 08:43
Full Text Link:
Related URLs: https://link.sp ... 723-022-09613-2 (Publisher URL)
PURE Output Type: Article
Published Date: 2022-09
Published Online Date: 2022-07-19
Accepted Date: 2022-06-03
Authors: Wang, Jiaojiao
Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Yu, Dongjin
Liu, Chang
Ma, Xiaoyu
Yu, Dingguo

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