[SAC13] Improving Business Process Model Quality Using Domain Ontologies

Revue Internationale avec comité de lecture : Journal Springer JoDS - Journal on Data Semantics , vol. 2(special issue on Evolution and Versioning in Semantic Data Integration Systems), pp. 75-87, 2013, (doi:10.1007/s13740-013-0022-4)

Mots clés: Information Systems Quality Domain knowledge Domain ontology Semantic quality Business process modeling Quality improvement

Résumé: This paper addresses the issue of improving quality of business process (BP) models by exploiting domain knowledge. Indeed, business process models reflect the business processes of companies. The success of these processes has a direct and undeniable impact on business operations success. Managing them through their underlying models helps improving their effectiveness, consistency, and transparency. BP modeling aims at a better understanding of processes, allowing deciders to achieve strategic goals of the company. However, several studies from literature showed that inexperienced system analysts often produce low level quality. This situation is partly due to lack of domain knowledge. In this paper we propose to support this modeling effort with an approach that uses domain knowledge to improve the semantic quality of BP models. We suggest to use ontologies as a mean to capture domain knowledge and meta-modeling techniques to deal with BP models independently of languages in which they are expressed. Our contribution is threefold: 1) the meta-models describing both a domain ontology and a BP model are described, 2) the alignment between the concepts of both meta-models is dened and illustrated, 3) a set of OCL (Object Constraint Language) mapping rules is provided. A simple case study illustrates the process.

Equipe: isid


@article {
title="{Improving Business Process Model Quality Using Domain Ontologies}",
author="S. Si-Said Cherfi and S. Ayad and I. Comyn-Wattiau",
journal="Springer JoDS - Journal on Data Semantics ",
number=special issue on Evolution and Versioning in Semantic Data Integration Systems,