Big data has been continuously generated from the rapidly developing of cloud/fog/edge computing, Internet of Things (IoT) and 5G technology. This big data not only brings great benefits and opportunities to human beings but also brings many risks and challenges. One major challenge is how to represent and treat higher-order and heterogeneous data from multi-sources. Tensors are emerging as powerful tools for representation and modeling of this data. Tensor decomposition can be used to extract potentially useful information from this data. Thus, it has attracted much attention from the big data community. The main target of this paper is to propose a novel data fusion framework to solve several main challenges of CPSS data applications, including CPSS big data representation, fusion, efficient computing, storage, robustness, and security issues. In this paper, we use many graphics to visualize complex tensor decomposition and transformation processes. The visualization may help the readers better understand tensor and tensor decomposition. It also provides a general guideline and a good starting point for those who are interested in tensor and tensor decomposition. Specifically, we first introduce the most extensively used matrix and tensor decomposition methods. Second, the current popular data fusion methods are reviewed and summarized. Third, we propose a novel tensor-network-conversion-based data fusion approach which can simultaneously analyze multiple matrices and multiple tensors. To better understand this approach, we give a brief review of tensor network. Fourth, based on the proposed approach, a novel CPSS big data fusion framework is proposed in this paper. Meanwhile, we verify it by a concise case study. Finally, some challenges and open problems of the proposed framework are discussed. The discussion also includes some exciting future research directions in the big data fusion field.

云/雾/边缘计算、物联网（IoT）和5G技术的快速发展不断产生大数据。大数据不仅给人类带来了巨大的利益和机遇，也带来了许多风险和挑战。一项主要挑战是如何表示和处理来自多源的高阶异构数据。张量正在成为表示和建模这些数据的强大工具。张量分解可用于从这些数据中提取潜在有用的信息。因此，它引起了大数据界的广泛关注。本文的主要目标是提出一种新的数据融合框架，以解决 CPSS 数据应用的几个主要挑战，包括 CPSS 大数据表示、融合、高效计算、存储、鲁棒性和安全问题。在本文中，我们使用许多图形来可视化复杂的张量分解和转换过程。可视化可以帮助读者更好地理解张量和张量分解。它还为那些对张量和张量分解感兴趣的人提供了一般指南和良好的起点。具体来说，我们首先介绍使用最广泛的矩阵和张量分解方法。其次，对当前流行的数据融合方法进行了回顾和总结。第三，我们提出了一种新的基于张量网络转换的数据融合方法，可以同时分析多个矩阵和多个张量。为了更好地理解这种方法，我们简要回顾一下张量网络。第四，基于所提出的方法，本文提出了一种新颖的CPSS大数据融合框架。同时，我们通过一个简明的案例研究来验证它。最后，讨论了所提出框架的一些挑战和开放问题。讨论还包括大数据融合领域一些令人兴奋的未来研究方向。