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收集和组合，以及时获得更可靠、更少的处理数据的机制。它通常用于涉及多个传感器的许多部门，如国防和安全、医疗诊断和手术、智能交通系统。随着我们进入数据泛滥的时代，不仅数据量大幅增加，而且还有各种各样的数据源，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结构化和非结构化数据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，目前的数据融合技术无法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.