Friday, 21 July 2017

Post Graduate Researcher Profiles - Michael Ojo

My name is Michael and I’m a doctorate researcher at the University of Bournemouth.  My research looks at  the “Architectural Framework for Intelligent Autonomic process management”.  My research work seeks to explore the combination of model-based approaches and artificial intelligence to provide self-sustaining solutions which will have huge impacts on business productivity.

With the advent of BigData and emerging technologies, business processes are becoming more complex with increasing levels of uncertainty.  The applicability of the current approaches to business challenges will soon be completely outpaced by this increasing complexity. Therefore, the need for data-driven system solutions which are smart, flexible, scalable, support dynamic composition and robust in the presence of change, arises.

The aim of autonomic computing is to address complexity inherent in software defined processes, by making processes/ systems to be self-managing.  But  we are in a digital age where innovations, technology, business goal and the cost of managing these business processes  are constantly changing (growing in complexity).  So there is need to develop a new way of applying autonomic computing paradigm to address this growing complexity.  This new way should be applicable to future business processes.

Model driven architecture (MDA) is the technique of defining processes, systems, as  interaction of diagrams or simply pictorial representation. For example if BT have a set of diagrams that describe a product and a code/document that describe the same product, in the future, It will be easier and faster for a BT new recruit to make innovations from that set of diagrams, than code/documents. This makes BT more future-ready for innovations. Technically, code/document is regarded as low-level abstraction while diagrams are termed higher level abstraction.

In a nutshell, we want to look into how we can apply MDA  in an autonomic  process to  improve business processes.

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