Title: From business process models to the cloud: a semantic approach

Authors: Beniamino Di Martino; Antonio Esposito; Giuseppina Cretella

Addresses: Department of Engineering, Università degli Studi della Campania 'Luigi Vanvitelli', Via Roma 29, Real Casa dell'Annunziata, Aversa (CE), Italy ' Department of Engineering, Università degli Studi della Campania 'Luigi Vanvitelli', Via Roma 29, Real Casa dell'Annunziata, Aversa (CE), Italy ' Department of Engineering, Università degli Studi della Campania 'Luigi Vanvitelli', Via Roma 29, Real Casa dell'Annunziata, Aversa (CE), Italy

Abstract: The fast evolution of IT services and the shift from server-centred applications to widely distributed frameworks and platforms, of which cloud computing is the best representative, have highly conditioned the way enterprise managers deal with their businesses. Several tools for the design and execution of business processes have been proposed, together with standards for their formal representation, such as BPMN. Despite the expressivity of such tools and representations, there is still a lack of integration with cloud services, mostly due to the high variety of currently available offers, which creates confusion among customers. Moreover, portability and interoperability issues tend to hinder the adoption of cloud solutions for the immediate deployment of business processes. In order to overcome such issues and support the adoption of cloud services for the deployment of business processes, in this paper, a semantic approach is proposed. The presented representation aims at easing the mapping process and to suggest users the best suitable cloud services to compose. A case study which demonstrates the applicability and efficacy of the approach is also described.

Keywords: business process; semantics; business process model notation; BPMN; OWL-S; cloud computing; ontologies; modelling; cloud services.

DOI: 10.1504/IJHPCN.2018.096718

International Journal of High Performance Computing and Networking, 2018 Vol.12 No.4, pp.368 - 378

Available online: 04 Dec 2018 *

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