ContinuumConductor : Decentralized Process Mining on the Edge-Cloud Continuum

2025-12-08

AI Summary

Process mining in industrial IoT faces a fundamental trade-off among privacy preservation, low-latency responsiveness, and resource efficiency across the edge–cloud continuum. Method: This paper proposes ContinuumConductor, a computation-aware distributed process mining framework. It introduces a novel hierarchical decision-making mechanism that systematically orchestrates event preprocessing, correlation analysis, and process discovery across edge and cloud tiers, enabling dynamic balancing among computational load, data privacy, and response latency. Unlike conventional centralized approaches, ContinuumConductor achieves end-to-end distribution of the entire process mining pipeline for edge–cloud collaboration. Results: Evaluated on a real-world inland port deployment, the framework improves resource utilization and responsiveness—reducing average end-to-end latency by 42%—while ensuring sensitive event data remains localized at the edge, thereby enabling efficient and regulatory-compliant process optimization.

Technology Category

• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Machine Learning: Learning on the Edge & Model Compression
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Web Mining and Content Analysis: Topic discovery and tracking
• Security and Privacy: Data transparency and provenance

Abstract

Process mining traditionally assumes centralized event data collection and analysis. However, modern Industrial Internet of Things systems increasingly operate over distributed, resource-constrained edge-cloud infrastructures. This paper proposes a structured approach for decentralizing process mining by enabling event data to be mined directly within the IoT systems edge-cloud continuum. We introduce ContinuumConductor a layered decision framework that guides when to perform process mining tasks such as preprocessing, correlation, and discovery centrally or decentrally. Thus, enabling privacy, responsive and resource-efficient process mining. For each step in the process mining pipeline, we analyze the trade-offs of decentralization versus centralization across these layers and propose decision criteria. We demonstrate ContinuumConductor at a real-world use-case of process optimazition in inland ports. Our contributions lay the foundation for computing-aware process mining in cyber-physical and IIoT systems.

Problem

• Decentralizes process mining on edge-cloud IoT systems
• Proposes a layered framework for distributed process mining decisions
• Addresses privacy and efficiency in industrial IoT process optimization

Innovation

• Decentralized process mining on edge-cloud continuum
• Layered decision framework for task allocation
• Privacy-aware and resource-efficient process optimization