Looking for Meaning: Discovering Action-Response-Effect Patterns in Business Processes

Process mining enables organizations to capture and improve their processes based on fact-based process execution data. A key question in the context of process improvement is how response s to an event (action) result in desired or undesired outcomes (effects). From a process perspective, this requires understanding the action-response patterns that occur. Current discovery techniques do not allow organizations to gain such insights. In this paper we present a novel approach to tackle this problem. We propose and formalize a technique to discover action-response-effect patterns. In this technique we use well-established statistical tests to uncover potential dependency relations between each response and its effect s on the cases. The goal of this technique is to provide organizations with processes that are: (1) appropriately represented, and (2) effectively filtered to show meaningful relations. The approach is evaluated on a real-world data set from a Dutch healthcare facility in the context of aggressive behavior of clients and the response s of caretakers.