Unlocking Non-Block-Structured Decisions: Inductive Mining with Choice Graphs
This work addresses a gap in process mining for organizations needing to analyze complex operational processes, though it is incremental as it builds on existing POWL advancements.
The paper tackled the limitation of inductive mining algorithms in accurately modeling non-block-structured decision points in process discovery by extending the Partially Ordered Workflow Language (POWL) with choice graphs, resulting in models that more precisely represent complex real-world decision-making without compromising scalability.
Process discovery aims to automatically derive process models from event logs, enabling organizations to analyze and improve their operational processes. Inductive mining algorithms, while prioritizing soundness and efficiency through hierarchical modeling languages, often impose a strict block-structured representation. This limits their ability to accurately capture the complexities of real-world processes. While recent advancements like the Partially Ordered Workflow Language (POWL) have addressed the block-structure limitation for concurrency, a significant gap remains in effectively modeling non-block-structured decision points. In this paper, we bridge this gap by proposing an extension of POWL to handle non-block-structured decisions through the introduction of choice graphs. Choice graphs offer a structured yet flexible approach to model complex decision logic within the hierarchical framework of POWL. We present an inductive mining discovery algorithm that uses our extension and preserves the quality guarantees of the inductive mining framework. Our experimental evaluation demonstrates that the discovered models, enriched with choice graphs, more precisely represent the complex decision-making behavior found in real-world processes, without compromising the high scalability inherent in inductive mining techniques.