Automatic business process model extension to repair constraint violations

Consider an artifact-centric business process model, containing both a data model and a process model. When executing the process, it may happen that some of the data constraints from the data model are violated. Bearing this in mind, we propose an approach to automatically generate an extension to the original business process model that, when executed after a constraint violation, repairs the contents of the data leaving it in a new consistent state.Peer ReviewedPostprint (author's final draft

Incremental inconsistency detection with low memory overhead

International audienceEnsuring models’ consistency is a key concern when using a model-based development approach. Therefore, model inconsistency detection has received significant attention over the last years. To be useful, inconsistency detection has to be sound, efficient, and scalable. Incremental detection is one way to achieve efficiency in the presence of large models. In most of the existing approaches, incrementalization is carried out at the expense of the memory consumption that becomes proportional to the model size and the number of consistency rules. In this paper, we propose a new incremental inconsistency detection approach that only consumes a small and model size-independent amount of memory. It will therefore scale better to projects using large models and many consistency rules