Repair of Unsound Data-Aware Process Models

Process-aware Information Systems support the enactment of business processes, and rely on a model that prescribes which executions are allowed. As a result, the model needs to be sound for the process to be carried out. Traditionally, soundness has been defined and studied by only focusing on the control-flow. Some works proposed techniques to repair the process model to ensure soundness, ignoring data and decision perspectives. This paper puts forward a technique to repair the data perspective of process models, keeping intact the control flow structure. Processes are modeled by acyclic Data Petri Nets. Our approach repairs the Constraint Graph, a finite symbolic abstraction of the infinite state-space of the underlying Data Petri Net. The changes in the Constraint Graph are then projected back onto the Data Petri Net.