Mining Attributed Interaction Networks On Industrial Event Logs

In future Industry 4.0 manufacturing systems reconfigurability and flexible material flows are key mechanisms. However, such dynamics require advanced methods for the reconstruction, interpretation and understanding of the general material flows and structure of the production system. This paper proposes a network-based computational sensemaking approach on attributed network structures modeling the interactions in the event log. We apply descriptive community mining methods for detecting patterns on the structure of the production system. The proposed approach is evaluated using two real-world datasets.