Abstract:
Abstract. Case handling is a new paradigm for supporting flexible and knowledge intensive business processes. It is strongly based on data as the typical product of these processes. Unlike workflow management, which uses predefined process control structures to determine what should be done during a workflow process, case handling focuses on what can be done to achieve a business goal. While classical Petri nets are a good theoretical foundation for workflow management, the data-intensive nature of case handling does not allow for the abstraction of data. Therefore, we use Colored Petri Nets (CPNs) as a foundation for case handling. This paper models the key principles of case handling in terms of CPNs and uses state-space analysis and simulation to validate the concepts. Moreover, we also link the CPN model to process mining and show that it is possible to rediscover case handling processes based on the event log of a CPN simulation.
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