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

2025-05-11

AI Summary

Existing process mining algorithms struggle to accurately model non-structured decision points, leading to inadequate representation of complex, non-nested, multi-exit branching logic prevalent in real-world business processes. Method: We propose an extension of the Process-Oriented Workflow Language (POWL) that introduces Choice Graphs to explicitly capture non-structured decisions, accompanied by a novel inductive discovery algorithm. Our approach preserves model hierarchy, soundness, and verifiability while relaxing traditional block-structured constraints to enable flexible and precise modeling of intricate branching behavior. Technical contributions include an extended syntax definition, formal semantics for Choice Graphs, algorithmic reconstruction, log parsing mechanisms, and structural consistency verification. Results: Experiments on multiple real-world event logs demonstrate significant improvements in recognition accuracy for non-structured decisions, while maintaining linear time complexity and strong scalability.

Technology Category

• Data Mining & Knowledge Management: Intelligent Query Processing
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Knowledge Representation and Reasoning: Description Logics

Application Category

• Web Mining and Content Analysis: Bridging structured and unstructured data
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Modeling non-block-structured decisions in process discovery
• Extending POWL to handle complex decision logic
• Preserving scalability while improving decision representation

Innovation

• Extends POWL with choice graphs
• Models non-block-structured decisions hierarchically
• Preserves inductive mining guarantees and scalability