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A model driven and clustering method for service identification directed by metrics
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2020-10-19 , DOI: 10.1002/spe.2913
Mohammad Daghaghzadeh 1 , Seyed Morteza Babamir 1
Affiliation  

Service identification (SI) in the life cycle of service‐oriented architecture is a critical phase. Business models consisting of business process (BP) model and business entity (BE) model are the useful models that may be used for SI. To this end, SI is carried out by partitioning activities in BP based on the activities' use of the entities in BE. However, a proper partitioning activities to services, which is called a service design, is a challenge. This article aims to present a semiautomatized clustering method for partitioning the activities to services, which is directed by new proposed metrics cohesion, coupling, and granularity. With regard to the conflict of the metrics, a multiobjective evolutionary algorithm (MOEA) is used to clustering activities where the metrics are considered as objectives should be optimized. The MOEA produces a set of optimal solutions as proper identified services of a service design. Finally, we used three case studies to show the effectiveness of the proposed method and then evaluated the results.

中文翻译:

一种基于度量的服务识别模型驱动聚类方法

面向服务架构生命周期中的服务标识(SI)是一个关键阶段。由业务流程 (BP) 模型和业务实体 (BE) 模型组成的业务模型是可用于 SI 的有用模型。为此,通过基于活动对 BE 中实体的使用划分 BP 中的活动来执行 SI。然而,将活动适当地划分为服务,这被称为服务设计,是一个挑战。本文旨在提出一种将活动划分为服务的半自动化聚类方法,该方法由新提出的度量内聚、耦合和粒度指导。针对度量的冲突,使用多目标进化算法(MOEA)对将度量视为应优化目标的活动进行聚类。MOEA 产生一组最佳解决方案,作为服务设计的适当识别服务。最后,我们使用三个案例研究来证明所提出方法的有效性,然后评估结果。
更新日期:2020-10-19
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