Yimu Guo

TL;DR: A hierarchical cyber-physical-social big data processing framework composed of three planes, namely, data representation and decomposition, data storage and processing, and data analysis and service is presented, in which tensor train and quantized TT decompositions are particularly introduced to remarkably overcome the curse of dimensionality.

TL;DR: A triple-plane EC architecture for IoT is proposed including the edge device plane, edge server plane, and cloud plane, respectively, which is conducive to collaboratively accomplishing the EC applications and significantly outperforms the state-of-the-art cloud-assisted mobile computing scheme.

TL;DR: Experimental results based on real-world GPS trajectory dataset demonstrate that TT-HODED algorithm can significantly improve the computation efficiency and reduce the running memory on the premise of guaranteeing the almost consistent prediction accuracy compared to the original H ODED algorithm.

TL;DR: Experimental results demonstrate that the proposed scheme outperforms the state-of-the-art scheduling schemes in SOO and the Pareto front in TOO can provide appropriate solutions to satisfy different application requirements.

TL;DR: This article aims to provide sustainable smart educational services including precise personalized recommendation and adaptive clustering under different contexts by correlatively analyzing the global educational data from multiple dimensions via incremental tensor decomposition.