Big Data Fusion

数据融合是一种将从多个来源获取的数据以一致的方式进行收集和组合，从而及时获得更可靠、更少量的处理数据的机制。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。随着我们进入一个数据泛滥的时代，不仅数据量大大增加，而且数据源种类繁多，如传感器测量和记录，以及网络空间（如社交网络）。这种令人难以置信的增长以及结构化和非结构化数据的可用性，在数量、速度和种类上造成了问题，这被称为大数据。然而，目前的数据融合技术并不能真正应对大数据带来的挑战。

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.