A BPMN extension for modeling non functional properties of business processes

TitleA BPMN extension for modeling non functional properties of business processes
Publication TypeConference Paper
Year of Publication2011
AuthorsBocciarelli, P., and Andrea D'Ambrogio
Conference NameSimulation Series
ISBN Number9781617828386
KeywordsAdministrative data processing, BPMN, Business Process, Business process management, Business process model, Business Process Modeling Notation (BPMN), Computer simulation, Enterprise resource management, MDA, Model driven architectures, Performance and reliabilities, Reliability, Software architecture, Systems engineering
AbstractBusiness Process Management (BPM) is an holistic approach for describing, analyzing, executing, managing and improving large enterprise business processes, which can be seen as collections of related tasks executed to accomplish well-defined goals. This paper introduces a notation for the description of a business process in terms of both functional and non-functional properties, specifically addressing the performance and reliability characterization of a business process. In the BPM context, the Business Process Modeling Notation (BPMN) is the de-facto standard for the high-level description of business processes. Unfortunately BPMN does not support the characterization of the business process in terms of non-functional properties such as performance and reliability. To overcome such limitation, this paper introduces PyBPMN (Performability-enabled BPMN), a lightweight BPMN extension for the specification of properties that address both performance and reliability. The proposed extension is based on an approach that exploits principles and standards introduced by the Model Driven Architecture (MDA), thus obtaining significant advantages in terms of easy customization and improved automation. The paper also presents an example application of the proposed extension to show how it enables the automated transformation of a business process model into a parameterized performance model whose execution gives insights about the process behavior.