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

网络物理社会系统中基于张量的数据表示和处理框架

• 批准号: RGPIN-2014-06326
• 负责人: Yang, LaurenceTianruo
• 金额: $ 1.7万
• 依托单位: St. Francis Xavier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759281
• 关键词: Tensor; based; Data; Representation; Processing

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.

嵌入式系统、无线通信、传感技术的蓬勃发展以及对云计算和社交网络的新兴支持，使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。这是一种新兴的计算范式，近年来引起了工业界和学术界的广泛关注。一般而言，CPS系统通过各种结构化/半结构化/非结构化格式的物理感知设备(Varity)从物理世界收集海量数据(Volume)，并立即响应用户的需求(Velocity)，在物理空间或社交空间为用户提供主动服务(准确性)。这些收集的大数据通常是高维、冗余和噪声的，超出了计算机系统的处理能力。随着CPS系统的快速发展，迫切需要设计一种新的数据表示和处理框架来应对日益增长的大规模和高维数据，该方案描述了一种基于张量的数据表示和处理框架，张量是一种广泛应用的高维矩阵。首先，我们将CPS数据投影到包括时间、空间位置、网络资源和用户在内的6阶张量上，建立了CPS系统中网络空间、物理空间和社会空间相结合的统一数据表示模型。在此基础上，提出了基于高效Lanczos方法的增量式分布式高阶奇异值分解(HOSVD)方案，对基于离线/在线流张量的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，任何处理器/机器都可以随时轻松参与大数据处理。针对各种机器/处理器和设备的任务映射，研究了新的调度算法，并在智能家居和交通等应用中进行了相应的案例研究，以验证所提出框架的可行性和灵活性。