Unlocking Non-Block-Structured Decisions: Inductive Mining with Choice Graphs

TL;DR

This paper extends inductive process discovery methods to model complex non-block-structured decision points using choice graphs, improving accuracy while maintaining scalability.

Contribution

It introduces choice graphs into POWL, enabling inductive mining to effectively capture non-block-structured decisions in process models.

Findings

Models with choice graphs better represent real-world decision complexity.

The approach maintains inductive mining's scalability and quality guarantees.

Experimental results show improved model accuracy.

Abstract

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.