Super Variants

Process mining offers methods to analyze the actual control-flow behavior of a process. The two main available methods are process discovery and variant analysis. While process discovery aggregates all variants into one model, the models often suffer from high complexity and lack detailed granularity. Conversely, variants are simple and offer fine granularity but their sheer number makes them difficult to comprehend. To bridge this gap, we introduce the concept of super variants. These represent a middle ground between the complexity of discovered process models and the simplicity of variants, offering an aggregation of closely related variants. We propose a super-variant mining framework based on object-centric variants, evaluate its scalability, and demonstrate its utility through a practical use case. This new approach promises to enhance control-flow analysis by striking a new balance between complexity and aggregation.