Compositional Discovery of Workflow Nets from Event Logs Using Morphisms

TL;DR

This paper introduces a modular method for discovering multi-agent system process models from event logs, utilizing morphisms to compose workflow nets while preserving soundness and improving model precision.

Contribution

It proposes a novel compositional scheme for workflow nets using morphisms, enabling sound and precise multi-agent process model discovery from event logs.

Findings

Enhanced model precision demonstrated

Workflow net composition preserves soundness

Applicable to asynchronous multi-agent interactions

Abstract

This paper presents a modular approach to discover process models for multi-agent systems from event logs. System event logs are filtered according to individual agent behavior. We discover workflow nets for each agent using existing process discovery algorithms. We consider asynchronous interactions among agents. Given a specification of an interaction protocol, we propose a general scheme of workflow net composition. By using morphisms, we prove that this composition preserves soundness of components. A quality evaluation shows the increase in the precision of models discovered by the proposed approach.