Big Data Fusion

大数据融合

• 批准号: RGPIN-2015-04938
• 负责人: Leung, Henry
• 金额: $ 3.42万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689432
• 关键词: Big; Data; Fusion

Data fusion is a mechanism for the data acquired from multiple sources to be gathered and combined in a consistent way to obtain more reliable, less voluminous processed data in a timely manner. It is conventionally used in many sectors involving multiple sensors, such as defense and security, medical diagnostic and surgery, intelligent transportation systems. As we are moving into an era of data deluge, not only is the amount of data greatly increasing, but there is a wide variety of types of data sources, such as sensor measurements and recordings, and cyberspace (e.g. social networks). This incredible growth and availability of both structured and unstructured data causing problems in volume, velocity and variety is termed big data. Current data fusion techniques, however, cannot really handle the challenges posted by big data.****In this project, we propose a multi-disciplinary approach for big data fusion. We will combine approximation theory in computer science, complexity theory in physics, and distributed and sparse systems in signal processing to develop novel methods for fusing massive amounts of data. In particular, we will focus on the three basic components of data fusion: registration, association and fusion. We propose using system sparseness and motion dynamic to develop a new registration model to align a large amount of devices. To associate or correlate the data, we will develop an approximate multi-frame nearest neighbor data association to speed up the association process and use complex networks to extract the deep correlations in the cyberspace to determine honest relationships. Since big data takes on a variety of formats, we propose to develop a universal data fusion based on first- and second- order statistics to provide a close-to-optimal data fusion.***The proposed research will focus on applications in three important industries: energy, wireless communications and the environment. The proposed research does not only develop fundamental data and signal processing algorithms for big data, but it also makes data fusion applicable to areas that will eventually involve big data. The big data industry is worth more than $100 billion and growing at 10% a year. By 2018, the United States alone could face a shortage of about 170,000 people in this area. The proposed research will prepare talented people for the rapidly growing market.**

数据融合是一种将从多个来源获取的数据以一致的方式收集和组合的机制，以便及时获得更可靠、处理量更少的数据。它通常用于涉及多个传感器的许多领域，例如国防和安全、医疗诊断和手术、智能交通系统。随着我们进入数据洪流时代，不仅数据量大幅增加，而且数据源的类型也多种多样，例如传感器测量和记录以及网络空间（例如社交网络）。结构化和非结构化数据的令人难以置信的增长和可用性导致了数量、速度和多样性方面的问题，这被称为大数据。然而，当前的数据融合技术无法真正应对大数据带来的挑战。****在这个项目中，我们提出了一种用于大数据融合的多学科方法。我们将结合计算机科学中的近似理论、物理学中的复杂性理论以及信号处理中的分布式和稀疏系统，开发融合大量数据的新方法。特别是，我们将重点关注数据融合的三个基本组成部分：注册、关联和融合。我们建议使用系统稀疏性和运动动态来开发新的配准模型来对齐大量设备。为了关联或关联数据，我们将开发近似多帧最近邻数据关联以加速关联过程，并使用复杂网络提取网络空间中的深层关联以确定诚实关系。由于大数据具有多种格式，我们建议开发一种基于一阶和二阶统计的通用数据融合，以提供接近最优的数据融合。***拟议的研究将重点关注三个重要行业的应用：能源、无线通信和环境。该研究不仅开发了大数据的基础数据和信号处理算法，而且使数据融合适用于最终涉及大数据的领域。大数据产业价值超过1000亿美元，并且以每年10%的速度增长。到2018年，仅美国就可能面临该领域约17万人的短缺。拟议的研究将为快速增长的市场培养人才。**