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The Journal of Supercomputing

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22.09.2023

Design-time business process compliance assessment based on multi-granularity semantic information

verfasst von: Xiaoxiao Sun, Siqing Yang, Chenying Zhao, Dongjin Yu

Erschienen in: The Journal of Supercomputing

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Abstract

Business process compliance is an essential part of business process management, which saves organizations from penalties caused by non-compliant processes. However, current researches on business process compliance mainly focus on checking using general constraint rules that have been formalized without in-depth analysis of related regulatory documents and mostly involve extensive human efforts. In this paper, we aim to propose an automatic and interpretable compliance checking approach for design-time business processes. By combining deep learning and a natural language processing approach based on rule templates, we extract semantic information from regulatory documents at different granularities for subsequent compliance checking. In addition, we match appropriate rules to the process model and detect the degree of violation of the business process from three control-flow perspectives. The effectiveness of this method is validated on two real-world datasets.

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Fußnoten
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Metadaten
Titel
Design-time business process compliance assessment based on multi-granularity semantic information
verfasst von
Xiaoxiao Sun
Siqing Yang
Chenying Zhao
Dongjin Yu
Publikationsdatum
22.09.2023
Verlag
Springer US
Erschienen in
The Journal of Supercomputing
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-023-05626-0