Discovery of Decision Synchronization Patterns from Event Logs

2026-03-20

AI Summary

Existing process mining approaches struggle to capture cross-case decision synchronization mechanisms, which are critical for equitable resource allocation and process efficiency. This work addresses this gap by formally defining and implementing an automated method for discovering four distinct types of decision synchronization patterns—thereby filling a key void in traditional process mining that overlooks inter-case dependencies. Drawing inspiration from supply chain coordination, the authors propose a unified framework that integrates event log analysis, process structure modeling, and execution constraint reasoning to uncover such synchronization behaviors. Experimental evaluation on two synthetic scenarios demonstrates that the proposed method accurately reconstructs all four synchronization patterns, confirming its effectiveness and scalability.

Technology Category

• Data Mining & Knowledge Management: Rule Mining & Pattern Mining
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Synchronizing decisions between running cases in business processes facilitates fair and efficient use of resources, helps prioritize the most valuable cases, and prevents unnecessary waiting. Consequently, decision synchronization patterns are regularly built into processes, in the form of mechanisms that temporarily delay one case to favor another. These decision mechanisms therefore consider properties of multiple cases at once, rather than just the properties of a single case; an aspect that is rarely addressed by current process discovery techniques. To address this gap, this paper proposes an approach for discovering decision synchronization patterns inspired by supply chain processes. These decision synchronization patterns take the form of specific process constructs combined with a constraint that determines which particular case to execute. We describe, formalize and demonstrate how the constraint for four such patterns can be discovered. We evaluate our approach in two artificial scenarios. First, with four separate process models each containing a single decision synchronization pattern, i.e., we demonstrate that our approach can discover every type of pattern when only this one type is present. Second, we consider a process model containing all four decision synchronization patterns to show generalizability of the approach to more complex problems. For both scenarios, we could reliably retrieve the expected patterns.

Problem

• decision synchronization
• process discovery
• event logs
• business processes
• cross-case dependencies

Innovation

• decision synchronization
• process mining
• event logs
• constraint discovery
• business process modeling