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.
翻译：传统流程挖掘通常假设事件数据的集中式收集与分析。然而，现代工业物联网系统日益在分布式、资源受限的边缘-云基础设施上运行。本文提出一种结构化方法，通过使事件数据能够在物联网系统的边缘-云连续体内直接进行挖掘，实现流程挖掘的去中心化。我们引入ContinuumConductor——一个分层决策框架，用于指导何时在中心或去中心化执行预处理、关联与发现等流程挖掘任务，从而实现隐私保护、响应迅速且资源高效的流程挖掘。针对流程挖掘流水线中的每个步骤，我们分析了跨这些层级的去中心化与集中化之间的权衡，并提出了决策标准。我们在内陆港口流程优化的实际用例中展示了ContinuumConductor的应用。我们的贡献为信息物理系统及工业物联网系统中计算感知的流程挖掘奠定了基础。
相关内容
微信扫码咨询专知VIP会员