Repairing Soundness Properties in Data-Aware Processes

Within the growing area of data-aware processes, Data Petri nets (DPNs) with arithmetic data have recently gained popularity thanks to their ability to balance simplicity with expressiveness. DPNs can be automatically mined from event data, but these process discovery techniques typically come without any correctness guarantees. In particular, the generated models may violate the crucial property of data-aware soundness. While data-aware soundness can be checked automatically for a large class of models, nothing is known about how to repair such processes once a violation is detected. In this paper we are concerned with repairing DPNs so that the refined model satisfies the desired soundness properties. Our approach is based on conservative behavioural changes, which are minimally invasive in the sense that the behaviour of the repaired model coincides with that of the original model except for (prefixes of) traces that caused the violation. We show experimentally that the approach can be used to repair unsound DPNs from the literature.