Resumen de Supporting user during the execution of declarative business process models in scenarios subject to uncertainty
The quality of business process models (i.e., software artifacts that capture the relations between the organizational units of a business) is essential for enhancing the management of business processes. However, such modelling is typically carried out manually. This can be quite challenging and be very time consuming in some real scenarios which present certain design requirements (i.e., estimated activity attributes, input uncertainty, relations between activities and resource allocation).
This situation is further complicated if such requirements have to be addressed together with some optimization requirements including flexibility and robustness. Moreover, the resulting models may be non-optimized, potentially contain errors, and might be too strict. To facilitate the human work and to improve the resulting business process models, this Thesis Dissertation proposes a software-supported approach for automatically generating optimized enactment plans from declarative specifications at design-time. For managing these plans the proposed approach suggests to build upon configurable business process models (which allow analysts to understand what these plans share and what their differences are).
Before the execution of the configurable business process model, a business process model has to be selected from it. This selection is typically performed by an analyst who manually individualizes the model in order to address the business requirements. To individualize such models, unlike existing approaches, a totally automated method to create a questionnaire-based application for guiding a business expert on individualizing the configurable business process model during run-time is proposed. Therefore, the decision of how the enactment plan to be executed looks like is deferred to run-time, i.e., when more information is available.
The current Thesis Dissertation differs from existing approaches since it considers the uncertainty of the scenario through stochastic attributes, as well as the optimization of multiple objective functions. Moreover, a questionnaire-based application is suggested to enable the selection of an optimized enactment plan from a declarative specification during run-time.
