企业架构评估方法的系统文献综述

IF 23.8 1区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Norbert Rudolf Busch, Andrzej Zalewski
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引用次数: 0

摘要

企业架构(EA)是设计和管理组织的信息系统组件、帮助优化资源、管理风险和促进变更的系统和整体方法。它权衡不同的体系结构质量属性,以获得最有利的体系结构。然而,企业环境评估缺乏系统的方法。本研究采用系统文献综述法,从2005年以来发表的3644篇论文中精选109篇进行详细分析。研究的主要结果表明,EA评估方法在世界范围内广泛采用的一个关键因素在于评估和体系结构建模过程的自动化,特别是强调数据收集方面。EA评估的自动化将使组织能够简化他们的过程,做出数据驱动的决策,并更有效地响应变化,最终有助于他们在全球市场上的竞争力和长期成功。本研究确定了多种评价方法,确定了评价标准,考察了这些方法在实践中的验证程度,并为进一步的研究和推进提供了方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Systematic Literature Review of Enterprise Architecture Evaluation Methods
Enterprise Architecture (EA) is a systematic and holistic approach to designing and managing an organization's information systems components, aiding in optimizing resources, managing risk, and facilitating change. It weighs different architectural quality attributes against each other to achieve the most advantageous architecture. However, the evaluation of EA lacks a systematic approach. This study employs a Systematic Literature Review, analyzing, in detail, 109 articles carefully selected from 3644 papers published since 2005. The key outcome of the research reveals that a crucial factor for the extensive worldwide adoption of EA evaluation methods lies in the automation of the assessment and architecture modeling processes, particularly emphasizing the facet of data collection. The automation of EA evaluation will empower organizations to streamline their processes, make data-driven decisions, and respond more effectively to change, ultimately contributing to their competitiveness and long-term success in the global market. The study identifies diverse evaluation methods, determines evaluation criteria, examines the extent to which these methods have been verified in practice, and provides directions for further research and advancement.
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来源期刊
ACM Computing Surveys
ACM Computing Surveys 工程技术-计算机:理论方法
CiteScore
33.20
自引率
0.60%
发文量
372
审稿时长
12 months
期刊介绍: ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.
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