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Modeling functional requirements for configurable content- and context-aware dynamic service selection in business process models

FRECE, Aleš, JURIČ, Matjaž B. Modeling functional requirements for configurable content- and context-aware dynamic service selection in business process models. J. vis. lang. comput., Aug. 2012, vol. 23, no. 4, str. 223-247, ilustr. http://www.sciencedirect.com/science/article/pii/S1045926X12000249

Povzetek

n this article, we propose a meta-model for formal specification of functional requirements for configurable content- and context-aware dynamic service selection in business process models with the objective to enable greater flexibility of the modeled processes. The dynamic service selection can cope with highly dynamic business environments that today's business processes must handle. Modeling functional requirements for dynamic service selection in business process models is not well covered in literature. Some partial solutions exist but none of them allows modeling a complete set of functional requirements for the selection similar to the one we are addressing in this article. Our meta-model enables formal specification of service selection relevant data extracted from service request message, custom configuration data (e.g., thresholds), process and task definition/instance metadata, and service selection rules. The meta-model is configurable and content- and context-aware. Processes leveraging our meta-model can adapt to changing requirements without redesign of the process flow. Proposed meta-model allows users to additionally configure the models at run time (e.g., raising a threshold). Modeling can be divided into roles with different required competences. We implement our meta-model in BPMN 2.0 (Business Process Model and Notation) through specific extensions to the BPMN semantic and diagram elements. By measuring complexity of real-world sample process models we show that using our solution modelers can efficiently model business processes that need to address frequent changing demands. Compared to available alternatives, models using our solution have on average ∼13% fewer activities, ∼16% fewer control-flow elements and ∼22% fewer control paths. By reading ∼10% smaller models (by volume) model readers get more flexible process models that capture all functional requirements for the dynamic selection.