Stochastic Workflow Nets Based Workflow Pattern Modeling

Petri nets are widely used in describing workflow models of management systems, and also functioning in process management and resource organizing. The number of workflow patterns are increasing along with the demands of business process modeling. The Petri net and its extensions can no longer meet the requirement of model description, due to the limitation of their semantics. The models of some patterns are very sophisticated and even grow exponentially in complexity. Also the performance of these new patterns are not well studied. This paper extends Petri nets to Stochastic workflow nets (SWNs) and introduces stochastic or state related variables into arc weights, transition enabling guard and execution time. With the new features, SWNs can describe models more accurately and flexibly. And performance of common patterns is calculated under the assumption of exponentially distributed time. The paper also illustrates how to simplify the model with equivalent patterns.