Process Mining on Distributed Data Sources
🤖 AI Summary
Traditional process mining relies on centralized, discrete event logs, rendering it inadequate for real-time, fine-grained, and heterogeneous event streams generated by distributed sensors in logistics, healthcare, and smart cities. Methodologically, this project advances three paradigm shifts: from offline to online, from centralized to distributed, and from log-based to sensor-driven process mining. It introduces the first distributed process intelligence framework tailored for continuous event streams, structured around a six-domain research agenda spanning infrastructure, data, and human-centered dimensions. The approach integrates formal modeling, distributed stream processing, privacy-preserving computation, and empirical evaluation, adhering to algorithm engineering principles for cross-layer co-design. Key contributions include a privacy-aware, scalable, and user-centric theoretical foundation and technology roadmap—enabling a new paradigm of responsive, decentralized process intelligence.
Application Category
📝 Abstract
Major domains such as logistics, healthcare, and smart cities increasingly rely on sensor technologies and distributed infrastructures to monitor complex processes in real time. These developments are transforming the data landscape from discrete, structured records stored in centralized systems to continuous, fine-grained, and heterogeneous event streams collected across distributed environments. As a result, traditional process mining techniques, which assume centralized event logs from enterprise systems, are no longer sufficient. In this paper, we discuss the conceptual and methodological foundations for this emerging field. We identify three key shifts: from offline to online analysis, from centralized to distributed computing, and from event logs to sensor data. These shifts challenge traditional assumptions about process data and call for new approaches that integrate infrastructure, data, and user perspectives. To this end, we define a research agenda that addresses six interconnected fields, each spanning multiple system dimensions. We advocate a principled methodology grounded in algorithm engineering, combining formal modeling with empirical evaluation. This approach enables the development of scalable, privacy-aware, and user-centric process mining techniques suitable for distributed environments. Our synthesis provides a roadmap for advancing process mining beyond its classical setting, toward a more responsive and decentralized paradigm of process intelligence.
Problem
Research questions and friction points this paper is trying to address.
Extending process mining to handle distributed, heterogeneous event streams
Adapting traditional techniques for online, decentralized sensor data analysis
Developing scalable, privacy-aware methods for distributed environments
Innovation
Methods, ideas, or system contributions that make the work stand out.
Online analysis replacing offline methods
Distributed computing over centralized systems
Sensor data integration beyond event logs
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