Yonglin Leng

Papers

Total Citations

H-Index

About

Yonglin Leng is a researcher whose work sits at the intersection of cloud robotics and data management, with a particular focus on the efficient handling of Resource Description Framework (RDF) data. In their most cited work, "A Partitioning and Index Algorithm for RDF Data of Cloud-Based Robotic Systems" (2018, 4 citations), Leng addresses a critical bottleneck in modern robotics: as robotic systems connect more terminals and sensors, the volume of heterogeneous RDF data grows exponentially, straining storage and retrieval capabilities. Leng’s key contribution is a novel partitioning and indexing algorithm designed to optimize the management of this data in cloud-based environments, enabling faster query responses and more scalable robotic operations. While the citation count is modest, the work is notable for tackling a practical, emerging challenge at the intersection of semantic web technologies and robotics—a field where efficient data handling is crucial for real-time decision-making. Leng’s research provides a foundational approach for future systems that rely on large-scale, heterogeneous data streams, making it a valuable reference for engineers and researchers working on cloud-connected robotic platforms.