Cloud-Edge Collaborative Scalable Tucker-Based Tensor Computations for Ubiquitous Consumer Electronics Data

Abstract

With the continuous expansion of consumer electronics applications, various data generated from ubiquitous consumer electronics devices are experiencing exponential growth. By leveraging the significant advantages of multidimensional association analysis, tensor-based big data technology has proven effective in uncovering hidden patterns within these data. However, the curse of dimensionality severely restricts the widespread exploitation of tensors, particularly on edge devices with limited computing and storage capabilities under cloud-edge computing environments. To address this challenge, we propose a series of cloud-edge collaborative scalable Tucker-based tensor computations to effectively analyze these ubiquitous data. First, we present a set of Tucker-based tensor operations that transform high-order and large-scale tensor operations into multiple low-order and small-scale operations while preserving the equivalence of their results. Then, we present a scalable Tucker-based computation architecture to adapt to the cloud-edge computing paradigm, including scalable inter-Tuckercore and intra-Tuckercore models. Furthermore, we implement some typical Tucker-based tensor computations based on various scalable models and analyze their complexity in detail. Finally, extensive evaluations on both synthetic and real-world datasets demonstrate that the proposed scalable Tucker-based tensor computation method significantly improves computational efficiency, achieving an average efficiency improvement of 2 to 5 times compared to serial computation. These results confirm its suitability for cloud-edge collaboration to process ubiquitous consumer electronics data.