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https://doi.org/10.1109/SNPD.2009.98

has been cited by the following article:

• TITLE: Enterprise Architecture: A Comparative Analysis of Validation Semantics and Heterogeneous Model Frameworks

  AUTHORS: Joe Essien

  KEYWORDS: Enterprise Architecture, Ontologies, Business Process Management, Validation

  JOURNAL NAME: Open Journal of Business and Management, Vol.11 No.5, August 23, 2023

  ABSTRACT: The advancement of Enterprise Architecture modelling techniques and the need to incorporate these models with other Information Systems collaborations require a methodology that ensures the consistency and compatibility of diverse models with shared business objectives. The fulfillment of this requirement is impeded by a notable obstacle, which is the growing intricacy of Enterprise Architecture Framework modelling tools and methodologies. Many organisations that have adopted a variety of methodologies face the difficulty of integrating models because of the existence of inconsistent and diverse modelling artefacts within an unreliable framework, which requires validation. The primary aim of this article is to conduct a comprehensive analysis of the existing methodologies for validating enterprise architecture and propose a new approach that focuses specifically on validating models. This research combines the Systematic Review research approach and meta-analysis research method to provide a systematic and comprehensive review and analysis of multiple studies and combining findings from these studies to synthesize evidence and draw definitive conclusions. The comparative analysis explores validation semantics and heterogeneous model frameworks and delves into different approaches used to validate models in the context of heterogeneous model integration. The study examines the strengths and weaknesses of various validation methods and frameworks for heterogeneous models, considering factors like accuracy, efficiency and scalability. By comparing different validation semantics and heterogeneous model frameworks, the analysis provides valuable insights into selecting appropriate techniques for effectively integrating and validating diverse models in complex systems.