TensorMon: A Breakthrough in Sparse Data Gathering Leveraging Tensor-Enhanced Techniques for System and Network Monitoring

Sparse data gathering has become a promising solution for reducing measurement costs by leveraging the inherent sparsity of data. However, most existing approaches rely on low-dimensional models such as compressive sensing or matrix completion, which are limited in capturing complex high-dimensional structures. To overcome these limitations, we propose TensorMon, a novel tensor-based sparse data gathering framework that introduces a cuboid sampling strategy to more effectively exploit multidimensional correlations. Unlike traditional entry-based or tube-based sampling, TensorMon introduces the innovative concept of cuboid sampling. We further develop a lightweight sampling scheduling algorithm and a non-iterative inference algorithm to ensure efficient measurement planning and accurate reconstruction of unmeasured data. Theoretical analysis establishes a new performance bound for our sampling strategy, which is significantly lower than those in existing literature. To validate our theoretical findings, we conduct extensive experiments on four real-world datasets: two network monitoring datasets, a city-scale crowd flow dataset, and a road traffic speed dataset. Experimental results demonstrate that TensorMon achieves substantial reductions in measurement cost, delivers high inference accuracy, and ensures rapid data recovery, highlighting its effectiveness and practicality across diverse application scenarios.