A tremendous challenge in the development of Cyber-Physical-Social Systems (CPSS) is to integrate the growing volume and wide variety of data generated from multiple sources. To address the challenge, this paper presents an integration framework on cloud consisting of five functionally complementary processes, namely data representation, dimensionality reduction, relation establishment, data rank and data retrieval. The unstructured, semi-structured and structured data in the cyber, physical and social space are first represented as low-order data tensors, and then transformed to a three-order feature tensor for relation establishment. A similarity-based multi-linear data rank approach is proposed to measure the importance of the CPSS big data, and an incremental method is explored to rapidly and accurately update the rank vector. This paper, through a smart home case study, illustrates a practical application of the proposed integration framework, and evaluates the performance of the multi-linear data rank approach as well as the incremental rank update method. The results reveal that the proposed framework is feasible and competitive to integrate the CPSS big data on cloud.

中文翻译：

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用于网络物理社会系统大数据的云上集成框架

网络物理社会系统 (CPSS) 发展的一个巨大挑战是整合从多个来源生成的不断增长的数量和种类繁多的数据。为了应对这一挑战，本文提出了一个云集成框架，由五个功能互补的过程组成，即数据表示、降维、关系建立、数据排序和数据检索。网络空间、物理空间和社会空间中的非结构化、半结构化和结构化数据首先被表示为低阶数据张量，然后转化为三阶特征张量进行关系建立。提出了一种基于相似度的多线性数据秩方法来衡量CPSS大数据的重要性，并探索了一种增量方法来快速准确地更新秩向量。这篇报告，通过一个智能家居案例研究，说明了所提出的集成框架的实际应用，并评估了多线性数据等级方法以及增量等级更新方法的性能。结果表明，所提出的框架在云上集成 CPSS 大数据方面是可行且具有竞争力的。