In workflow management systems (WFMSs), time management plays an essential role in controlling the lifecycle of business processes. Especially, run-time analysis of time constraints is necessary to help process manger proactively detect possible deadline violations and appropriately handle these violations. Traditional time constraint analyses either present deterministic results which are too restrictive in highly uncertain workflow processes, or only consider static analysis at workflow build-time. For such an issue, this paper proposes a dynamic approach for analyzing time constraints during process execution. To be specific, based on a Petri-net-extended stochastic model, this approach first analyzes activity instancesȁ9; continuous probabilities of satisfying time constraints when a process instance is initiated. Afterwards, during the execution of this process instance, the approach dynamically updates these probabilities whenever an activity instance is completed. Moreover, an example process instance in real-world WFMSs shows the practicality of our approach.
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