业务架构集成中的企业级业务组件识别方法（英文）

发布时间：2018-04-12 14:47

本文选题：业务架构集成 + 业务组件　；参考：《Frontiers of Information Technology & Electronic Engineering》2017年09期

【摘要】：基于组件的军事信息系统业务架构集成是军事领域中一个重要研究内容,而识别企业级业务组件是业务架构集成中一个关键问题。当前业务组件识别的方法多是关注于软件层面业务组件,忽略了诸如组织、资源等企业级因素;而目前企业级业务组件识别方法被证明非常低效。因此本文提出一种企业级业务组件识别的新方法,该方法全面考虑了业务组件的内聚度、耦合度、粒度、可维护性、可复用性五个设计原则。首先基于业务组件模型和DoD AF(Department of Defense Architecture Framework)模型对业务组件进行了定义和形式化描述,为了对业务组件进行定量化分析,将业务模型转为一个CRUD(create,read,update,and delete)矩阵并提出了6类指标;然后将业务组件识别问题转化为一个多目标优化问题,并采用了模拟退火遗传算法(simulated annealing hybrid genetic algorithm,SHGA)进行求解。最后通过案例分析验证了本文方法较先前的方法对企业级业务组件识别具有更好的适用性和高效性。
[Abstract]:The business architecture integration of military information systems based on components is an important research content in military field, and the identification of enterprise business components is a key issue in business architecture integration.The current methods of business component identification are mostly focused on the software level business components, ignoring the enterprise factors such as organization and resources, while the current methods of business component identification have been proved to be very inefficient.In this paper, a new method of business component identification is proposed, which takes into account five design principles: cohesion, coupling, granularity, maintainability and reusability.Firstly, the business component is defined and formalized based on the business component model and the DoD AF(Department of Defense Architecture framework model. In order to analyze the business component quantitatively, the business model is transformed into a crud model, and six kinds of indexes are proposed.Then, the problem of business component identification is transformed into a multi-objective optimization problem, and simulated annealing hybrid genetic algorithm is used to solve the problem.Finally, a case study shows that the proposed method is more applicable and efficient than the previous method in identifying enterprise business components.
【作者单位】： Science
【基金】：Project supported by the National Natural Science Foundation of China(No.71571189)
【分类号】：F272;TP18

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