Big Data Fusion

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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.