Authors:
Samia Oussena
;
Joe Essien
and
Peter Komisarczuk
Affiliation:
University of West London, United Kingdom
Keyword(s):
Web Ontology, Enterprise Architecture, Metamodel, Model, Viewpoint, Resource Description Framework Schema, Modelling Language, Archimate, Business Strategy, Validation, Motivation.
Abstract:
Formalization of Enterprise Architecture (EA) concepts as a whole is an area which has continued to constitute a major obstacle in understanding the principles that guide its adaptations. Ubiquitous use of terms such as models, meta-models, meta-meta-models, frameworks in the description of EA taxonomies and the relationship between the various artefacts has not been exclusive or cohesive. Consequently variant interpretations of schemas, conflicting methodologies, disparate implementation have ensued. Incongruent simulation of alignment between dynamic business architectures, heterogeneous application systems and validation techniques has been prevalent. The divergent and widespread paradigm of EA domiciliation in practice makes it even more challenging to adopt a generic formalized constructs in which models can be interpreted and verified (Martin et al., 2004). The unavailability of a unified EA modelling language able to describe a wide range of Information Technology domains comp
ounds these challenges leading to exponentiations of EA perspectives. This paper seeks to present a formalization of concepts towards addressing validation concerns of EA through the use of ontologies and queries based on constraints specified in the model’s motivation taxonomy. The paper is based on experimental research and grounded on EA taxonomies created using the ArchiMate modelling language and open source web ontology. It delves into the use of semantics triples, Resource Description Framework Schema and relational graphs to map EA taxonomy artefacts into classes and slots using end-to-end conventional formalization approach applicable within heterogeneous EA domains. The paper also expounds on a proposal that postulates implementation of the approach, enables formalized traceability of EA validation and contributes to effective validation of EA through refined taxonomy semantics, mappings and alignment of motivation.
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