Leveraging Model-Driven Monitoring for Event-Driven Business Process Control

Business process execution generates great amounts of information on process results and intermediate activities. Most of this information is represented by events linked to their related process execution. Process monitoring uses such events and links to correlate incoming events to their corresponding process instances. These links may however get lost in the process of event capturing or not even be present in distributed process environments like logistics. Nonetheless, monitoring depends on the correct correlation of events in such scenarios. Furthermore, the correlation of external data like weather and tra c information, which has no connection to process executions, can be useful in the planning, execution and monitoring of processes as well. The approach presented in this paper uses semantic technologies to automatically identify those process executions that are related to the data of an occurring event. It uses linked data principles and graph-based algorithms to detect relatedness of events and process instances. The approach allows for the inclusion of external data and the correlation of external events without relying on process specific queries.