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 |Pages: 16
DOI: 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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The literature provides a lot of work in service identification approaches, ranging from top-down to bottom-up. In this section, we briefly review the most relevant work in service identification.

(Kazemi et al., 2011) have presented an automated method for identifying business services by adopting design metrics based on top-down decomposition of processes. This method takes a set of enterprise business processes as input and produces a set of non-dominated solutions representing appropriate business services using a multi-objective genetic algorithm.

(Azevedo et al., 2009) proposed a top-down approach for services identification from business process models, applying heuristics to define services from the semantic analysis of process elements such as business rules and business requirements, and from a syntactic analysis of process models according to its corresponding structural patterns.

(Kang et al., 2008) presented a method of service identification using ontology for product line. Primary, a Semantic relationship is derived through the mapping between feature modeling and ontology. Second, both service and service boundary are defined by semantic distance. Third, the method is proposed for feature grouping and candidate service refining service candidate which is the fittest service granularity.

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