Analysis of large workflows

We present a new net-reduction methodology to facilitate the analysis of large workflow models. We propose an enhanced algorithm based on reducible subnet identification which preserve both soundness and response time distribution. Moreover we outline an approach to model the dynamic behavior of business processes by exploiting the power of the class of non-Markovian stochastic Petri net models

Business process model abstraction

In order to execute, study, or improve operating procedures, companies document them as business process models. Often, business process analysts capture every single exception handling or alternative task handling scenario within a model. Such a tendency results in large process specifications. The core process logic becomes hidden in numerous modeling constructs. To fulfill different tasks, companies develop several model variants of the same business process at different abstraction levels. Afterwards, maintenance of such model groups involves a lot of synchronization effort and is erroneous. We propose an abstraction technique that allows generalization of process models. Business process model abstraction assumes a detailed model of a process to be available and derives coarse-grained models from it. The task of abstraction is to tell significant model elements from insignificant ones and to reduce the latter. We propose to learn insignificant process elements from supplementary model information, e.g., task execution time or frequency of task occurrence. Finally, we discuss a mechanism for user control of the model abstraction level – an abstraction slider

Aide à la décision pour les plans climat énergie territoriaux : exploration des interactions à l'aide d'une simulation dynamique multi-agents

International audienceno abstrac