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An Intelligent Graph Edit Distance-Based Approach for Finding Business Process Similarities

Abid Sohail1, Ammar Haseeb1, Mobashar Rehman2,*, Dhanapal Durai Dominic3, Muhammad Arif Butt4
1 Department of Computer Science, COMSATS University Islamabad, Lahore, Pakistan
2 Faculty of Information and Communication Technology, Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia
3 Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Tronoh Perak, Malaysia
4 Punjab University College of Information Technology, University of the Punjab, Lahore, Pakistan
* Corresponding Author: Mobashar Rehman. Email:
(This article belongs to this Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 69(3), 3603-3618. https://doi.org/10.32604/cmc.2021.017795

Received 11 February 2021; Accepted 20 April 2021; Issue published 24 August 2021

Abstract

There are numerous application areas of computing similarity between process models. It includes finding similar models from a repository, controlling redundancy of process models, and finding corresponding activities between a pair of process models. The similarity between two process models is computed based on their similarity between labels, structures, and execution behaviors. Several attempts have been made to develop similarity techniques between activity labels, as well as their execution behavior. However, a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them. However, neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity. To that end, we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models. Furthermore, we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences. Finally, we have evaluated the proposed approach using our generated collection of process models.

Keywords

Machine learning; intelligent data management; similarities of process models; structural metrics; dataset; graph edit distance; process matching; artificial intelligence

Cite This Article

A. Sohail, A. Haseeb, M. Rehman, D. Durai Dominic and M. Arif Butt, "An intelligent graph edit distance-based approach for finding business process similarities," Computers, Materials & Continua, vol. 69, no.3, pp. 3603–3618, 2021.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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