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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(1): 46-51 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.88

An Approach towards Enterprise Architecture Analysis using AHP and Fuzzy AHP

M. Razavi Davoudi and K. Sheikhvand

Abstract—Enterprise Architecture (EA) as a discipline that manages large amount of models and information about different aspects of the enterprise can support decision making on enterprise-wide issues. In order to provide such support, EA information should be amenable to analysis of various utilities and quality attributes. In this regard, this paper provides an approach based on Fuzzy Analytical Hierarchy Process (AHP) towards EA analysis. It proposes an extended quantitative method of assessing quality attribute achievement of different scenarios using AHP and Fuzzy AHP based on the knowledge and experience of EA experts and domain experts. Due to the uncertainty in the judgments of participants, the crisp pair-wise comparison in the conventional AHP is insufficient and imprecise to capture the right judgments. Therefore, a fuzzy logic is introduced in the pair-wise comparison of AHP in one of steps of the method. The applicability of the proposed approach is demonstrated using a practical case study.

Index Terms—Enterprise architecture analysis, quality attribute, analytical hierarchy process, fuzzy logic, fuzzy AHP.

M. Razavi Davoudi. is with the Islamic Azad University, Central Tehran Branch, Poonak, Tehran, Iran (e-mail: mah.razavi@iauctb.ac.ir).
K. Sheikhvand, was with Islamic Azad University, Central Tehran Branch, Poonak, Tehran, Iran. He is now with the Politecnico di Milano, Milan, Italy (e-mail: kourosh.sheykhvand@mail.polimi.it)

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Cite: M. Razavi Davoudi and K. Sheikhvand, "An Approach towards Enterprise Architecture Analysis using AHP and Fuzzy AHP," International Journal of Machine Learning and Computing vol. 2, no. 1, pp. 46-51, 2012.

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