Paul Becker

Gesellschaft zur Förderung angewandter Informatik

Papers

Total Citations

H-Index

About

Paul Becker’s research lies at the nexus of cloud computing, fog architectures, and predictive analytics for industrial cyber-physical systems. His work is foundational in demonstrating how distributed data processing—spanning cloud, edge, and fog layers—can unlock real-time insights, detect anomalies, and forecast equipment failures in manufacturing and robotics. His most cited paper (2020, 46 citations) proposes a cloud-to-edge framework that enables proactive maintenance strategies, moving beyond reactive repairs to anticipate disruptions. This work, along with his earlier studies on fog computing for predictive maintenance (17 citations) and cloud-to-edge architectures (11 citations), has shaped how industries approach operational resilience. Becker’s contributions are especially impactful for the robotics sector, where smooth production depends on early failure detection. By bridging theoretical models with practical deployment, he has helped define a new paradigm for smart manufacturing—one where data flows seamlessly from sensors to the cloud, empowering engineers to act before problems arise. His research continues to influence both academic discourse and industrial adoption of predictive analytics.