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

Technical evolution, heading for industry 4.0, makes companies tend to automate their management and operation, ideally defining it through business process models. To describe policies or rules related to the execution order of the activities in an organization, Declarative Business Process Models permit a 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 better to 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 models including both time patterns and multiple instances is proposed. This new model can be used to prognosticate if current process instances may violate a defined model in the future, according to the analysis of the robustness of the process instances evolution. The proposed Model-Based Prognosis is based on analyzing the event traces that represent the current instances and propagate their possible progression through the Constraint Programming paradigm. To ascertain if the model could be violated, it is analyzed how its robustness can tackle unexpected behaviors. To complete the formalization and modeling, an implementation applied to a real medical example is included in the paper. The prognosis of concurrent instances is addressed, dealing with formalized time and activity patterns even considering the resource availability, and getting acceptable execution times. The automatic verification and prognosis of declarative business processes are addressed considering concurrency and synchronization of multiple instances, performing well in terms of execution time. (C) 2020 Elsevier B.V. All rights reserved.