Prognosis of multiple instances in time-aware declarative business process models

Abstract

Technological evolution, heading for industry 4.0, makes companies tend to automate their managementand operation, ideally defining it through business process models. To describe policies or rules relatedto the execution order of the activities in an organization, Declarative Business Process Models permita relaxed description of activity order, which needs monitoring to detect non-conforming behaviors.Commonly, the detection of a violation implies that the malfunction has already occurred, being betterto avoid the violation in advance. To predict future violations, prognosis is required.To allow the modeling of real business behavior, an extension of declarative business process modelsincluding both time patterns and multiple instances is proposed. This new model can be used to prog-nosticate if current process instances may violate a defined model in the future, according to the analysisof the robustness of the process instances evolution. The proposed Model-Based Prognosis is based onanalyzing the event traces that represent the current instances and propagate their possible progressionthrough the Constraint Programming paradigm. To ascertain if the model could be violated, it is analyzedhow its robustness can tackle unexpected behaviors.To complete the formalization and modeling, an implementation applied to a real medical example isincluded in the paper. The prognosis of concurrent instances is addressed, dealing with formalized timeand activity patterns even considering the resource availability, and getting acceptable execution times.The automatic verification and prognosis of declarative business processes are addressed consideringconcurrency