A new method for performance evaluation of enterprise architecture using streotypes
These days, we see many organizations with extremely complex systems with various processes, organizational units, individuals, and information technology support where there are complex relationships among their various elements. In these organizations, poor architecture reduces efficiency and flex...
Main Authors: | , , |
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Format: | Article |
Language: | English |
Published: |
Growing Science
2013-11-01
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Series: | Management Science Letters |
Subjects: | |
Online Access: | http://www.growingscience.com/msl/Vol3/msl_2013_320.pdf |
Summary: | These days, we see many organizations with extremely complex systems with various processes, organizational units, individuals, and information technology support where there are complex relationships among their various elements. In these organizations, poor architecture reduces efficiency and flexibility. Enterprise architecture, with full description of the functions of information technology in the organization, attempts to reduce the complexity of the most efficient tools to reach organizational objectives. Enterprise architecture can better assess the optimal conditions for achieving organizational goals. For evaluating enterprise architecture, executable model need to be applied. Executable model using a static architectural view to describe necessary documents need to be created. Therefore, to make an executable model, we need a requirement to produce products of the enterprise architecture to create an executable model. In this paper, for the production of an enterprise architecture, object-oriented approach is implemented. We present an algorithm to use stereotypes by considering reliability assessment. The approach taken in this algorithm is to improve the reliability by considering additional components in parallel and using redundancy techniques to maintain the minimum number of components. Furthermore, we implement the proposed algorithm on a case study and the results are compared with previous algorithms. |
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ISSN: | 1923-9335 1923-9343 |