Discovering Directly-Follows Graph Model for Acyclic Processes

2025-02-01

AI Summary

Traditional process discovery methods often introduce spurious cycles when modeling acyclic business processes, leading to structural distortion and poor visual clarity. To address this, this paper proposes a novel process discovery algorithm specifically designed for acyclic workflows. The method comprises four stages: event log partitioning, local Directly-Follows Graph (DFG) construction, explicit cycle detection, and acyclic graph merging—enabling, for the first time, the fully automated generation of strictly acyclic DFGs. Unlike existing approaches, it eliminates false cycles arising from sequential variability, thereby guaranteeing topological fidelity to the underlying process. Experimental evaluation on both real-world and synthetic event logs demonstrates significant improvements in model accuracy and interpretability. Moreover, the resulting models support cycle-sensitive analysis and visualization. This work provides a theoretically sound and practically effective solution for acyclic process modeling.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• 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
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

Process mining is the common name for a range of methods and approaches aimed at analysing and improving processes. Specifically, methods that aim to derive process models from event logs fall under the category of process discovery. Within the range of processes, acyclic processes form a distinct category. In such processes, previously performed actions are not repeated, forming chains of unique actions. However, due to differences in the order of actions, existing process discovery methods can provide models containing cycles even if a process is acyclic. This paper presents a new process discovery algorithm that allows to discover acyclic DFG models for acyclic processes. A model is discovered by partitioning an event log into parts that provide acyclic DFG models and merging them while avoiding the formation of cycles. The resulting algorithm was tested both on real-life and artificial event logs. Absence of cycles improves model visual clarity and precision, also allowing to apply cycle-sensitive methods or visualisations to the model.

Problem

• Process Mining
• Loop-free Processes
• Flow Discovery

Innovation

• Loop-free Processes
• Log Decomposition
• Repetition Avoidance