A Tensor-based Data Representation and Processing Framework in Cyber-Physical-Social Systems

嵌入式系统、无线通信、传感技术以及对云计算和社交网络的新兴支持的蓬勃发展和快速发展使得研究人员和实践者能够创建各种各样的网络物理社会（CPS）系统，这些系统智能地推理、自主地行动并且以上下文和情境感知的方式响应用户的需求。CPS系统是计算、通信和控制与物理世界、人类知识和社会文化元素的集成。CPS是一种新兴的计算模式，近年来引起了工业界和学术界的广泛关注。CPS系统通常通过各种物理感知设备（Variety）以结构化/半结构化/非结构化的形式从物理世界收集海量数据（Volume），并在物理空间或社会空间中对用户的需求进行即时响应（Velocity）和主动服务（Veracity）。这些收集的大数据通常是高维的，冗余的和嘈杂的，并且超出了计算机系统的处理能力。随着CPS系统的快速发展，迫切需要一种新的数据表示和处理框架来科普日益增长的大规模和高维数据，本文提出了一种基于张量的CPS系统数据表示和处理框架。首先，将CPS数据投影到包含时间、空间位置、网络资源和用户的6阶张量上，建立CPS系统中网络空间、物理空间和社会空间的统一数据表示模型。在此基础上，提出了基于Lanczos方法的增量分布式高阶奇异值分解（HOSVD）方法，对离线/在线流张量CPSS大数据进行降维和快速预处理。该方案将适用于任何设备和任何处理器/机器随时轻松参与大数据处理。本文将研究各种机器/处理器和设备任务映射的新调度算法，以及在智能家居和交通等应用中的相应案例研究，以验证所提出的框架的可行性和灵活性。

The booming growth and rapid development in embedded systems, wireless communications, sensing techniques and emerging support for cloud computing and social networks have enabled researchers and practitioners to create a wide variety of Cyber-Physical-Social (CPS) Systems that reason intelligently, act autonomously, and respond to the users’ needs in a context and situation-aware manner. The CPS systems are the integration of computation, communication and control with the physical world, human knowledge and sociocultural elements. This is a novel emerging computing paradigm and has attracted wide interests from both industry and academia in recent years.Generally, CPS systems collect massive data (Volume) from the physical world by various physical perception devices (Variety) in structured /semistructured/unstructured format and respond to the users’ requirements immediately (Velocity) and provide the proactive services (Veracity) for them in physical space or social space. These collected big data are normally high dimensional, redundant and noisy, and beyond the processing capacity of computer systems. With the rapid development of CPS systems, a novel data representation and processing framework should be urgently devised to cope with the increasing large scale and high dimensional data.This proposal describes a data representation and processing framework in CPS systems based on tensors, a type of high dimensional matrix widely used in many applications. First of all, we project the CPS data onto a 6-order tensor which includes time, spatial location, cyber resources, as well as users, and establish a unified data representation model with the integration of cyber, physical and social spaces in CPS systems. Then, the incremental distributed Higher-Order Singular Value Decomposition (HOSVD) scheme based on the effecient Lanczos method is proposed to perform the dimensionality reduction and quick preprocessing on the offline/online streaming tensor-based CPSS big data. The scheme will be suitable for any devices and any processor/machines easily participate in big data processing at any time. New scheduling algorithms for various machines/processors and devices’ task mapping will be investigated, as well as the corresponding case studies in some applications such as smart home and traffics to validate the feasibility and flexibility of the proposed framework.