Discovery of Decision Synchronization Patterns from Event Logs

TL;DR

This paper introduces a novel process discovery approach for identifying decision synchronization patterns in event logs, addressing a gap in current techniques by considering multiple cases simultaneously.

Contribution

The paper proposes a formal method to discover decision synchronization patterns inspired by supply chain processes, including four specific pattern constraints, and demonstrates its effectiveness on artificial scenarios.

Findings

Successfully discovers all pattern types in simple models

Effectively identifies patterns in complex models with multiple patterns

Approach reliably retrieves expected patterns in evaluated scenarios

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.