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Services Derivation from Business Process: A PSO-based Multi-Objective Approach

Services Derivation from Business Process: A PSO-based Multi-Objective Approach

Mohamed El Amine Chergui, Sidi Mohamed Benslimane
Copyright: © 2016 |Volume: 7 |Issue: 1 |Pages: 16
ISSN: 1947-9220|EISSN: 1947-9239|EISBN13: 9781466691766|DOI: 10.4018/IJARAS.2016010104
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MLA

Chergui, Mohamed El Amine, and Sidi Mohamed Benslimane. "Services Derivation from Business Process: A PSO-based Multi-Objective Approach." IJARAS vol.7, no.1 2016: pp.59-74. http://doi.org/10.4018/IJARAS.2016010104

APA

Chergui, M. E. & Benslimane, S. M. (2016). Services Derivation from Business Process: A PSO-based Multi-Objective Approach. International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS), 7(1), 59-74. http://doi.org/10.4018/IJARAS.2016010104

Chicago

Chergui, Mohamed El Amine, and Sidi Mohamed Benslimane. "Services Derivation from Business Process: A PSO-based Multi-Objective Approach," International Journal of Adaptive, Resilient and Autonomic Systems (IJARAS) 7, no.1: 59-74. http://doi.org/10.4018/IJARAS.2016010104

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Abstract

Several approaches for services development in SOA (Service Oriented Architecture) suggest business processes as a starting point. However, there is a lack of systematic methods for services identification during business analysis. It is recognized that in service engineering, service identification plays a critical role as it lays the foundation for the later phases. Existing Service identification approaches are often prescriptive and mostly ignore automation principles, most are based on the architect's knowledge thus could result in non-optimal designs which results in complicated dependencies between services. In this paper the authors propose a top down approach to identify automatically services from business process by using several design metrics. This approach produces services from business processes as input and using an improved combinatorial particle swarm optimization algorithm with crossover of genetic algorithm. The experimentation denotes that the authors' approach achieves better results in terms of performance and convergence speed.

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