Mahmood Ahmad email@example.com
Semantic derivation of enterprise information architecture from riva-based business process architecture
Contemporary Enterprise Information Architecture (EIA) design practice in the industry still suffers from issues that hamper the investment in the EIA design. First and foremost of these issues is the shortcoming of EIA design research to bridge the gap between business and systems (or information) architectures. Secondly, contemporary developed business process architecture methods, and in particular object-based ones have not been fully exploited for EIA design and thus widening the gap between business processes and systems. In practice, knowledge-driven approaches have been thoroughly influencing EIA design. Thirdly, the lack of using knowledge representation methods adversely affected the automation (or semi-automation) of the EIA design process. Software Engineering (SE) technologies and Knowledge Representation using ontologies continue to prove instrumental in the design of domain knowledge. Finally, current EIA development methods have often resulted in complex designs that hampered both adopting and exploiting EIA in medium to large scale organisations.
This research is aimed at investigating the derivation of the EIA from a given semantic representation of object-based Business Process Architecture (BPA), and in particular Riva-based BPA using the design science research-based methodology. The key design artefact of this research is the development of the BPAOntoEIA framework that semantically derives EIA from a semantic representation of Riva-based BPA of an enterprise. In this framework, EIA elements were derived from the semantic Riva BPA elements and associated business process models, with forward and backward traceability from/to the derived EIA to/from the original BPA. The BPAOntoEIA framework has been evaluated using the semantic Cancer Care and Registration BPA in Jordan. This framework has been validated using an authentic concern-based evaluation framework employing both static and dynamic validation approaches.
The BPAOntoEIA framework contributes to bridging the gap between the business and systems world by providing a business/IT alignment through the EIA derivation process, and using the semantic knowledge of business processes within the resultant EIA. A major novel contribution is the introduction of new evaluation metrics for EIA design, which are quantitative, and are not only indicative of the quality of the semantic EIA derivation from the associated BPA but also the extent of utilising
business process knowledge and traceability amongst EIA elements.
Amongst other novel contributions is the semantic EIA derivation process that comprises a suite of the Semantic Web Rules Language (SWRL) rules applied on the semantic BPA elements. The derivation scheme utilises the generic EIA (gEIAOnt) ontology that was developed in this research and represents a semantic meta-model of EIA elements of a generic enterprise. The resultant EIA provides a highly coherent semantic information model that is in-line with the theory of EIA design, semantically enriched, and fully utilises the semantic knowledge of business processes.
Benefits of this research to industry include the semantic EIA derivation process and a resultant information model that utilises the semantic information of business processes in the enterprise. Therefore, this enables the enterprise strategic management to plan for a single, secure and accessible information resource that is business process driven, and enabled in an agile environment. The semantic enrichment of the EIA is a starting point for a simplistic design of a domain-independent semantic enterprise architecture for the development of systems of systems in loosely coupled enterprises.
|APA6 Citation||Ahmad, M. Semantic derivation of enterprise information architecture from riva-based business process architecture. (Thesis). University of the West of England|
|Keywords||enterprise information architecture, semantic derivation of EIA, BPAOntoEIA|