A Discovery Technique for Expressive Yet Sound Process Models

Process discovery enables organizations to analyze and improve their operations by automatically deriving process models from event logs. While the Inductive Mining framework is widely adopted for ensuring model soundness, its strict block-structured nature often fails to capture the true complexity of real-world control flows. Recent advancements, such as the Partially Ordered Workflow Language (POWL), have relaxed these constraints for concurrency, yet a significant gap remains in effectively modeling complex decision logic and unstructured loops. We bridge this gap by introducing POWL 2.0, an extended modeling language that integrates choice graphs to represent non-block-structured decisions and cyclic flows within a hierarchical framework. In this paper, we present a robust inductive discovery algorithm that leverages POWL 2.0, and we explore several mining strategies for choice graphs. These strategies range in strictness to resolve the ambiguity between genuine loops and hidden concurrency. Our experimental evaluation demonstrates that the proposed approach captures complex behaviors without compromising the scalability and quality guarantees of the Inductive Mining framework.