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KPI for Blockchain: A Model-Driven and AI-Assisted Framework for Blockchain Performance Evaluation | ||
| Future Research on AI and IoT | ||
| مقاله 1، دوره 2، شماره 2، دی 2026، صفحه 1-17 اصل مقاله (4.01 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22080/frai.2026.31194.1051 | ||
| نویسندگان | ||
| Kimiya Karimi Dehkordi؛ Leila Samimi-Dehkordi* ؛ Abbas Horri | ||
| Department of Computer Engineering, Faculty of Technology and Engineering, Shahrekord University | ||
| تاریخ دریافت: 15 بهمن 1404، تاریخ بازنگری: 28 بهمن 1404، تاریخ پذیرش: 01 اسفند 1404 | ||
| چکیده | ||
| Blockchain technology has become a fundamental infrastructure for decentralized and trustworthy data management across a wide range of application domains. As blockchain systems increasingly operate in large-scale, intelligent, and interconnected environments, systematic performance evaluation has emerged as a critical challenge. Existing studies predominantly focus on isolated performance indicators or platform-specific benchmarking, lacking a unified and structured representation of performance dimensions and their interdependencies. This study proposes KPIforBlockchain, a model-driven framework designed to systematically represent, organize, and analyze key performance indicators (KPIs) in blockchain systems. The framework introduces an extensible metamodel that explicitly captures relationships between blockchain features and multi-dimensional performance indicators, including performance, cost, scalability, security, decentralization, and auxiliary factors. A set of formally defined computational indicators is integrated into the framework to support structured analytical reasoning. The applicability and analytical capability of the proposed framework are demonstrated through representative case-study blockchain models, illustrating how diverse configurations can be evaluated in a unified manner without relying on empirical benchmarking data. In addition, large language model–assisted generation of blockchain models is investigated, and the quality of generated models is assessed using precision, recall, and F1-score metrics under different prompting strategies. A metamodel-level structural comparison with existing KPI-oriented approaches further evaluates maintainability, understandability, and extensibility. The results demonstrate that KPIforBlockchain provides a consistent and expressive foundation for structured blockchain performance evaluation, while AI-assisted modeling significantly enhances model generation quality. The proposed framework supports comparative analysis and informed decision-making and lays the groundwork for future intelligent and automated blockchain evaluation mechanisms. | ||
| کلیدواژه‌ها | ||
| Blockchain performance evaluation؛ Key Performance Indicators (KPI)؛ Metamodel-based analysis؛ Model-Driven Engineering (MDE)؛ Large Language Models (LLMs) | ||
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آمار تعداد مشاهده مقاله: 96 تعداد دریافت فایل اصل مقاله: 229 |
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