Diagnosing and repairing data anomalies in process models

Abstract

When using process models for automation, correctness of the models is a key requirement. While many approaches concentrate on control flow verification only, correct data flow modeling is of similar importance. This paper introduces an approach for detecting and repairing modeling errors that only occur in the interplay between control flow and data flow. The approach is based on Petri nets and detects anomalies in BPMN models. In addition to the diagnosis of the modeling errors, a subset of errors can also be repaired automatically. © 2010 Springer-Verlag.