Using Machine Learning Techniques for Evaluating the Similarity of Enterprise Architecture Models

Borozanov, Vasil; Hacks, Simon; Silva, Nuno

doi:10.1007/978-3-030-21290-2_35

Cited by 10 publications

(4 citation statements)

References 19 publications

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“…The pursuit of more non-professional participation [17], rapid model development and validation, and high Return on Investment (ROI) for model development around a specific purpose can lead to reduced model quality, which challenges EAR management. Researchers noticed such potential repository pollution issues and proposed to use machine learning techniques to avoid adding duplicates to the repository [18].…”

Section: Ear Management and Data Reusementioning

confidence: 99%

A Method for Enterprise Architecture Model Slicing

Guo

Gao

et al. 2022

Applied Sciences

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Enterprise Architecture (EA) has been applied widely in industry as it brings substantial benefits to ease communication and improve business-IT alignment. However, due to its high complexity and cost, EA still plays a limited role in many organizations. Existing research recommends realizing more of the EA potential. EA can be developed for specific purposes, accumulated in a digital repository, and reused when needed later. Due to the diversity and inconsistency of the repository, it is challenging to find relevant EA data and reuse it. In the present research, we propose using slicing techniques to extract EA models for reuse. We validate the method with an official EA repository hosted by The Open Group. The result shows that the method could facilitate extracting existing EA model components for developing new EA artifacts to save cost, alleviate maintenance effort, and help keep the repository consistent for future (re)use.

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Section: Ear Management and Data Reusementioning

confidence: 99%

A Method for Enterprise Architecture Model Slicing

Guo

Gao

et al. 2022

Applied Sciences

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“…Cheng and Cheng [19] extracted the similarity of plant features using the Association Rule-Based Similarity (ARBS) method. Borozanov et al [20] developed an architectural model to search for similar measures and components in enterprise architecture and used the association rules to find interesting elements.…”

Section: Related Workmentioning

confidence: 99%

Geographic data-mining and extraction of association rules using the Apriori algorithm (Case study: Capital of Iranian provinces)

Houshmand-Nanehkaran

Lajevardi

Mahlouji-Bidgholi

2021

Preprint

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Extracting of association rules between urban features provides latent and considerable information for urban planners about the relationships between urban characteristics and their similarities. For this purpose, in this paper, the most famous and well-known Apriori algorithm is used. We present the Fariori algorithm to delay the characteristics that can be deleted during execution, as well as to achieve main and frequent features in the early stages with efficient changes to the Apriori algorithm. Although the spatial and temporal complexity of both algorithms is exponential based on the number of fea-tures, in practice, by implementing the Fariori algorithm in MATLAB, we achieved more rules than the existing software (R, Weka, Market Basket Analysis and, Yarpiz). In the proposed algorithm, it is possible to determine the degree of similarity by adjust-ing the support and confidence ratio parameters to identify a coherent set of similar cities. The used database includes cities of 31 in the provincial capitals of Iran. Dis-covering the association rules leads to similar cities finding and can be an efficient aid in the decision-making process.

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“…Moreover, EA modeling can be applied for specific validation or evaluation. For example, EA properties evaluation [32][33][34][35][36][37] and business application evaluation [38,39].…”

Section: Introductionmentioning

confidence: 99%

Empirically Modeling Enterprise Architecture Using ArchiMate

Zhi¹,

Zhou²

2022

Computer Systems Science and Engineering

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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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Using Machine Learning Techniques for Evaluating the Similarity of Enterprise Architecture Models

Cited by 10 publications

References 19 publications

A Method for Enterprise Architecture Model Slicing

A Method for Enterprise Architecture Model Slicing

Geographic data-mining and extraction of association rules using the Apriori algorithm (Case study: Capital of Iranian provinces)

Empirically Modeling Enterprise Architecture Using ArchiMate

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