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An unstable state is a state that must change due to actions within the domain (an internal event) while a stable state is a state that does not change unless forced to by action of the environment (an external event). A successful process is a sequence of unstable states of the domain, leading to a stable state, which reflects the process goal. A domain is represented by a set of state variables, each depicting the value of a relevant property of the domain at a given time. The focus of analysis is a domain, which is a part of the world. 2 The Generic Process Model (GPM) This section provides an informal and brief presentation of the ontological statebased view of a process, which we employ for our analysis. GPM is a notation-independent framework for analyzing business processes based on Bunge's ontology and its adaptation to information systems. Our analysis is based on the Generic Process Model (GPM). In this paper we suggest real-world semantics to splitting and merging in process models, and a framework to enable a systematic analysis of splitting and merging configurations.
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Hence, a clear distinction of the different situations represented by splitting and merging elements is expected to assist process designers in producing logically correct models. We believe this situation can lead to modeling errors. Hence, this is a case of construct overload. However, while this may provide forĢ Pnina Soffer, Yair Wand, and Maya Kaner easy visual representation, in essence, splitting and joining stand for different realworld situations. In addition, split and merge of the same logical type typically have the same graphical notation. In a few cases (e.g., BPMN) there are specific constructs that can express more complicated behavioral patterns, a variety of which are depicted by workflow patterns.
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Usually, AND and XOR constructs are available, sometimes an OR too (e.g., EPC ). Third, the available notation for splitting and merging is usually not expressive enough for representing and distinguishing the different cases of possible process behaviors. Second, splitting and merging structures are major sources of logical errors in process models (e.g., deadlocks and lack of synchronization ). First, while splitting and merging structures in process modeling languages are frequently well-defined formally (e.g., ), they often do not convey a welldefined ontological meaning. The motivation for this work is threefold. This paper aims at systematically analyzing, defining, and distinguishing the different types of phenomena that are described by splitting and merging in business processes. 1 Introduction The possible flows in business process execution are determined by points where parallel or alternative process paths might be taken, or where such paths merge. We use the model to suggest classification of control configurations and identify configurations that assure the enacted process can always reach its goal. This model is notationindependent and enables incorporating goals into process analysis. The state changes are controlled by laws which model the actions allowed in the domain. A process is modeled in terms of state changes of the domain in which the process occurs. We use an ontologically-based model of business processes to analyze the control configurations that can exist in a process model. However, despite the importance of process flow control and workflow patterns, no way exists yet to assure that a particular set of patterns is complete and non-redundant. Workflow patterns reflect possible executions of different configurations of splits and joins. Such elements usually appear in graphic models as splits and joins of activity sequences. Control flow elements are important in process models. 1 Semantic Analysis of Flow Patterns in Business Process Modeling Pnina Soffer 1, Yair Wand 2, and Maya Kaner 3 1 University of Haifa, Carmel Mountain 31905, Haifa 31905, Israel 2 Sauder School of Business, The University of British Columbia, Vancouver, Canada 3 Ort Braude College, Karmiel 21982, Israel Abstract.