Detection of Similarity Between Business Process Models with the Integration of Semantics in Similarity Measures

EasyChair Preprint 9342

Abstract

Business process models play an important role in today’s organizations and they are stored in models repositories. Organi-zations need to handle hundreds or even thousands of process models within their model repositories, which serve as a knowledge base for business process management. Similarity measures can detect similarities between Business process models and consequently they play an important role in the management of business processes. Existing researches are mostly based on the syntactic similarities based on labels of activities and deal with mapping of type 1:1. To address the problem, semantic similarities remain difficult to detect and this problem is accentuated when dealing with mapping of type n:m and considering large models. In this paper, we will present a solution for detecting similarities between business process models by taking into account the semantics. We will use a genetic algorithm, which is a well-known metaheuristic, to find a good enough mapping between two process models.

Keyphrases: Business Process Models, Genetic Algorithm, matching, semantics, similarity measures

@booklet{EasyChair:9342,
  author    = {Wiem Kbaier and Sonia Ayachi Ghannouchi},
  title     = {Detection of Similarity Between Business Process Models with the Integration of Semantics in Similarity Measures},
  howpublished = {EasyChair Preprint 9342},
  year      = {EasyChair, 2022}}