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Higher degree by research students

Shamila Mafazi

Doctor of Philosophy

Information Technology Engineering and the Environment Divisional Office

Sch Info Tech & Math Sciences

Division of Information Technology, Engineering and the Environment

Email: shamila.mafazi@mymail.unisa.edu.au


About me


I am a PhD Student with the Knowledge and Software Engineering Laboratory of the Advanced Computing Research Centre at University of South Australia.

I am interested in Software Engineering and Artificial Intelligence, particularly Business Process Model Management. Accordingly, my research focuses on creating business process model views and their co-evolution.

In the next few months I hope to successfully complete my PhD studies and make a contribution to the fields of Business Process Model Management and Software Engineering in the process.


Thesis


Research proposal: Consistent specialisation and adaptation of workflow models for preserving semantic constraints

Thesis: Consistent abstraction and co-elevation of business process models

Exploring and understanding large business process models are important tasks in the context of business process management. In recent years, several techniques have been proposed for the abstraction of business processes. Automated abstraction techniques have been devised for verifying correctness and consistency of process models and for providing customised process views for business process analysts. Yet a goal-focused and semantic-based approach in order to generate purposeful abstraction of business processes is an open issue.

We propose an approach for configuration of process abstractions with respect to a specific abstraction goal expressed as constraints on the correspondence relation between concrete and abstract process and process transformation operators. Our framework goes beyond simple structural aggregation and leverages domain-specific properties, taxonomies, meronymy, and flow criteria to generate a hierarchy of abstract process models. We outline the constraint-based framework, describe how rewriting-based abstraction mechanisms are embedded with consistency criteria guiding the search for abstractions, and show how notions of behaviour consistency can be utilised to obtain abstractions that conform to behavioural process inheritance criteria.

Another issue which this research deals with relates to the co-evolution of business processes at the same or different abstraction levels. Business processes are changed frequently due to internal and external factors, such as change in business strategy or emerging a new technology. Different views can be changed by different users concurrently while applying concurrent change operators on different views may result in conflicts or overlaps. Related work addressing this problem is based on execution trace analysis which is performed in a post-analysis phase and can be complex when dealing with large business process models.

We propose a design-based approach that can efficiently check consistency criteria and propagate changes on-the-fly from a process view to its reference process and related process views. In case of a conflict a type discrimination on the central artifact of a process is introduced and resolves the conflict.

http://search.library.unisa.edu.au/record/UNISA_DIGITOOL60985


Research publications


S. Mafazi, W. Mayer, G. Grossmann and M. Stumptner, A knowledge-based approach to the configuration of business process model abstractions, in Int. Workshop on Knowledge-Intensive Business Processes (KiBP 2012), CEUR-WS Vol.861, (CEURWS, 2012).

S. Mafazi, G. Grossmann, W. Mayer and M. Stumptner, On-the-Fly Change Propagation for the Co-evolution of Business Processes, in Proc. of OTM, LNCS 8185, (Springer, 2013), pp. 75-93.

S. Mafazi, W. Mayer and M. Stumptner, Conict Resolution for On-the-y Change Propagation in Business Processes, in Proc. of APCCM, CRPIT 154, (ACS, 2014).

S. Mafazi, G. Grossmann, W. Mayer, M. Schre and M. Stumptner, Consistent abstraction of business processes based on constraints, Journal on Data Semantics, (Springer, 2014), pp.1-20.


Associations


Knowledge and Software Engineering Laboratory

Advanced Computing Research Centre

Australian Computer Society