Uppsats
Analyzing common structures in Enterprise Architecture modeling notations
Master-uppsats
Stockholms universitet/Institutionen för data- och systemvetenskap
Publicerad: 2024
Språk: Engelska
Sammanfattning
Enterprise Architecture (EA) serves as a foundational framework for understanding and managing the structure of organizations from both business and technical perspectives. Within the realm of EA, modeling notations play a crucial role in representing the various components and their interrelations. However, the proliferation of different EA modeling notations has led to a complex landscape, with an abundance of element types catering to diverse needs. Therefore, this research endeavors to address this challenge by proposing a systematic analysis of existing EA modeling notations to identify common structures and properties. The overarching aim is to develop a customizable EA modeling framework that optimizes the presentation of element types based on specific user requirements. Through a comprehensive review and analysis, this research seeks to distill the essential components and relationships prevalent across different EA modeling notations. Drawing inspiration from the meta-object facility (MOF) utilized in UML diagrams, this study developed layers of metamodels that encapsulate these common properties. The study's systematic review and analysis identified TOGAF, ArchiMate, FEAF, and IAF as the most used EA frameworks. Then, the taxonomy was developed to classify EA elements across three dimensions: Architecture, Behavior, and Lifecycle. Thus, the representation of common elements through a multilevel model was achieved. Practical examples were created to demonstrate the application and effectiveness of the developed metamodels in enhancing the clarity and flexibility of EA modeling. By providing a standardized yet adaptable framework, this research pursuits to contribute to the advancement of enterprise architecture practices and facilitate more efficient and effective organizational design and management.
Information
- Författare
- Abyshev, Rustem
- Lärosäte / institution
- Stockholms universitet/Institutionen för data- och systemvetenskap
- Publiceringsdatum
- 2024
- Uppsatstyp
- Master-uppsats
- Språk
- Engelska
Utforska vidare
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