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Empirically Modeling Enterprise Architecture Using ArchiMate

Qiang Zhi1,*, Zhengshu Zhou2,3

1 Graduate School of Engineering, Tokyo Institute of Technology, Tokyo, CO 1528550, Japan
2 Graduate School of Informatics, Nagoya University, Nagoya, Aichi, CO 4648601, Japan
3 Institute of Innovation for Future Society, Nagoya University, Nagoya University, Nagoya, Aichi, CO 4648601, Japan

* Corresponding Author: Qiang Zhi. Email: email

Computer Systems Science and Engineering 2022, 40(1), 357-374. https://doi.org/10.32604/csse.2022.018759

Abstract

Enterprise Architecture (EA) has evolved based on the practice of information systems architecture design and implementation. EA is a rigorous description of a structure, and the objectives of EA modeling not only include clarifying corporate strategies, visualizing business processes, and modeling information systems to manage resources but also include improving organizational structures, adjusting information strategies, and creating new business value. Therefore, EA models cover a wide scope that includes both IT and business architectures. Typically, EA modeling is the initial and most important analysis step for researchers, architects, and developers. ArchiMate is the dominant modeling language for EA and it has been shown to improve visualization of EA models. However, few studies have systematically introduced general modeling methods using ArchiMate to meet a broad range of needs and few studies have empirically evaluated the modeling method using ArchiMate. This paper introduces an EA visualization approach to fill that gap and conducts a case study on a wilderness weather station system. Strict controlled experiments are conducted to verify the effectiveness and feasibility of this modeling method, and the experimental results show that this modeling procedure is not only feasible, but also can effectively transform the system metamodel and restore the EA scene.

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Cite This Article

Q. Zhi and Z. Zhou, "Empirically modeling enterprise architecture using archimate," Computer Systems Science and Engineering, vol. 40, no.1, pp. 357–374, 2022.

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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