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Modeling Business Process and Events

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Intelligent Algorithms in Software Engineering (CSOC 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1224))

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Abstract

Business processes (BPs) are nowadays essential elements and key assets in any commercial organization. Additionally, BP matters are important issues in the context of enterprise computing. Business process management (BPM) is the main research area for process-aware systems involving methodologies, models, and supporting tools for process design, execution, and monitoring. BPM offers many challenges for software developers, including process specification and documentation. This paper is about (operational) BPs (e.g., procurement, hiring a new employee, supply chain management, request for leave), with a focus on modeling BPs in which all sub-processes, activities, data flows, inputs, and outputs, together with their relationships with each other, are identified and described. Typically, the focus of traditional process modeling is persistently on diagrammatic tools to design process notations (e.g., UML and BPMN). In this paper, we adopt a diagrammatic modeling methodology called the Thinging Machine (TM) and use it as a base to promote understanding of notions of BPs by offering a new perspective that captures a system’s dynamic behavior based on its events. The TM emphasizes a single unifying ontological element: the thing/machine concept (thimac), in contrast to object- or process-oriented methodologies. TM-based modeling can be a valuable tool in the general area of BPM. We partially demonstrate that by applying it to document two actual administrative systems. The resultant conceptual descriptions reflect a well-defined approach to the notions of processes and events.

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Correspondence to Sabah Al-Fedaghi .

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Al-Fedaghi, S., Makdessi, M. (2020). Modeling Business Process and Events. In: Silhavy, R. (eds) Intelligent Algorithms in Software Engineering. CSOC 2020. Advances in Intelligent Systems and Computing, vol 1224. Springer, Cham. https://doi.org/10.1007/978-3-030-51965-0_32

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