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A Road-map for Mining Business Process Models via Artificial Intelligence Techniques

Year 2022, Volume: 5 Issue: 1, 27 - 51, 28.06.2022

Afifi Chaima , Ali Khebızı

https://doi.org/10.53508/ijiam.1036234

Abstract

Nowadays, the size of data recorded and stored in enterprises information systems (IS) is increasing every second. To face to this phenomenon, contemporary technologies play a major role for gathering, analyzing, storing, and distributing data that enables organizations to make smart decisions and to take full control of their activities.
The traditional Business Process (BP) mining techniques were intensively used to discover, monitor, and optimize processes from event-logs without needing any priory model. However, the majority of the suggested algorithms have exhibited their limits (such as discovering nested loops, managing duplicate and hidden tasks as well as dealing with concurrent processes). In parallel, recent advances in the Artificial Intelligence (AI) discipline have generated a great deal of enthusiasm in a large spectrum of research area. In this perspective, AI methods emerge as one of the pillars to overcome the drawbacks of the conventional approaches allowing anomalies detection, prediction and recommendation tasks on ongoing process instances at run-time.
The aim of this work is to explore towards the use of AI techniques in the field of business process mining by presenting a state-of-the-art review ranging from traditional PM approaches to AI ones, as well as outlining a prospective road-map for mining business process models basing on AI techniques.

Keywords

Business process Business process management Business process modeling Process mining Machine learning Deep learning

Supporting Institution

8 mai 45 university guelma, Algeria

References

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Year 2022, Volume: 5 Issue: 1, 27 - 51, 28.06.2022

Afifi Chaima , Ali Khebızı

https://doi.org/10.53508/ijiam.1036234

Abstract

References

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There are 83 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Articles
Authors

Afifi Chaima

Ali Khebızı 0000-0002-5372-8201

Publication Date June 28, 2022
Acceptance Date January 6, 2022
Published in Issue Year 2022 Volume: 5 Issue: 1

Cite

APA Chaima, A., & Khebızı, A. (2022). A Road-map for Mining Business Process Models via Artificial Intelligence Techniques. International Journal of Informatics and Applied Mathematics, 5(1), 27-51. https://doi.org/10.53508/ijiam.1036234
AMA Chaima A, Khebızı A. A Road-map for Mining Business Process Models via Artificial Intelligence Techniques. IJIAM. June 2022;5(1):27-51. doi:10.53508/ijiam.1036234
Chicago Chaima, Afifi, and Ali Khebızı. “A Road-Map for Mining Business Process Models via Artificial Intelligence Techniques”. International Journal of Informatics and Applied Mathematics 5, no. 1 (June 2022): 27-51. https://doi.org/10.53508/ijiam.1036234.
EndNote Chaima A, Khebızı A (June 1, 2022) A Road-map for Mining Business Process Models via Artificial Intelligence Techniques. International Journal of Informatics and Applied Mathematics 5 1 27–51.
IEEE A. Chaima and A. Khebızı, “A Road-map for Mining Business Process Models via Artificial Intelligence Techniques”, IJIAM, vol. 5, no. 1, pp. 27–51, 2022, doi: 10.53508/ijiam.1036234.
ISNAD Chaima, Afifi - Khebızı, Ali. “A Road-Map for Mining Business Process Models via Artificial Intelligence Techniques”. International Journal of Informatics and Applied Mathematics 5/1 (June 2022), 27-51. https://doi.org/10.53508/ijiam.1036234.
JAMA Chaima A, Khebızı A. A Road-map for Mining Business Process Models via Artificial Intelligence Techniques. IJIAM. 2022;5:27–51.
MLA Chaima, Afifi and Ali Khebızı. “A Road-Map for Mining Business Process Models via Artificial Intelligence Techniques”. International Journal of Informatics and Applied Mathematics, vol. 5, no. 1, 2022, pp. 27-51, doi:10.53508/ijiam.1036234.
Vancouver Chaima A, Khebızı A. A Road-map for Mining Business Process Models via Artificial Intelligence Techniques. IJIAM. 2022;5(1):27-51.

International Journal of Informatics and Applied Mathematics