Mining context-dependent and interactive business process maps using execution patterns

Process mining techniques attempt to extract non-trivial knowledge and interesting insights from event logs. Process models can be seen as the "maps" describing the operational processes of organizations. Unfortunately, traditional process discovery algorithms have problems dealing with less-structured processes. Furthermore, existing discovery algorithms do not consider the analyst’s context of analysis. As a result, the current models (i.e., "maps") are difficult to comprehend or even misleading. To address this problem, we propose a two-phase approach based on common execution patterns. First, the user selects relevant and context-dependent patterns. These patterns are used to obtain an event log at a higher abstraction level. Subsequently, the transformed log is used to create a hierarchical process map. The approach has been implemented in the context of ProM. Using a real-life log of a housing agency we demonstrate that we can use this approach to create maps that (i) depict desired traits, (ii) eliminate irrelevant details, (iii) reduce complexity, and (iv) improve comprehensibility.