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: 6541
Jiong Fu, Xue-shan Luo, Ai-min Luo, Jun-xian Liu. Enterprise-level business component identification in business architecture integration[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(9): 1320-1335.
@article{title="Enterprise-level business component identification in business architecture integration",
author="Jiong Fu, Xue-shan Luo, Ai-min Luo, Jun-xian Liu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="9",
pages="1320-1335",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601836"
}
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%A Ai-min Luo
%A Jun-xian Liu
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%V 18
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601836
TY - JOUR
T1 - Enterprise-level business component identification in business architecture integration
A1 - Jiong Fu
A1 - Xue-shan Luo
A1 - Ai-min Luo
A1 - Jun-xian Liu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 9
SP - 1320
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Y1 - 2017
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1601836
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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