Full Text:   <2604>

Summary:  <1884>

CLC number: TP303

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2017-09-30

Cited: 0

Clicked: 7283

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jiong Fu

http://orcid.org/0000-0001-8298-141X

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Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.9 P.1320-1335

http://doi.org/10.1631/FITEE.1601836


Enterprise-level business component identification in business architecture integration


Author(s):  Jiong Fu, Xue-shan Luo, Ai-min Luo, Jun-xian Liu

Affiliation(s):  Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China

Corresponding email(s):   jiongfu@nudt.edu.cn, xsluo@nudt.edu.cn, amluo@nudt.edu.cn, 18674864900@163.com

Key Words:  Business architecture integration, Business component, Component identification, Create, read, update, and delete (CRUD) matrix, Heuristic



Abstract: 
The component-based business architecture integration of military information systems is a popular research topic in the field of military operational research. Identifying enterprise-level business components is an important issue in business architecture integration. Currently used methodologies for business component identification tend to focus on software-level business components, and ignore such enterprise concerns in business architectures as organizations and resources. Moreover, approaches to enterprise-level business component identification have proven laborious. In this study, we propose a novel approach to enterprise-level business component identification by considering overall cohesion, coupling, granularity, maintainability, and reusability. We first define and formulate enterprise-level business components based on the component business model and the Department of Defense Architecture Framework (DoDAF) models. To quantify the indices of business components, we formulate a create, read, update, and delete (CRUD) matrix and use six metrics as criteria. We then formulate business component identification as a multi-objective optimization problem and solve it by a novel meta-heuristic optimization algorithm called the ‘simulated annealing hybrid genetic algorithm (SHGA)‘. Case studies showed that our approach is more practical and efficient for enterprise-level business component identification than prevalent approaches.

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