LIU Xin1, WANG Xiao2,3, ZHANG Weishan2, WANG Jianji4, WANG Feiyue2,3,5
1.College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580 2.The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190 3.Parallel Workshop, Qingdao Academy of Intelligent Industries, Qingdao 266111 4.Institute of Artificial Intelligence and Robotics, Xi′an Jiaotong University, Xi′an 710049 5.Research Center of Military Computational Experiments and Parallel Systems,National University of Defense Technology, Changsha 410073
Abstract：For many real world applications, data available from actual scenes are generally incomplete and conditional. Therefore, a mechanism of generating big data from small data and then producing small but precise knowledge for specific problems is extremely useful. To this end, the concept and the framework of parallel data are proposed and discussed. Parallel data consist of virtual data from experimental computing and real data collected for actual problems. Actual and virtual data interact and co-evolute in parallel, making virtual and actual complement, thus enabling the process of transferring big data to data intelligence for general problem solving. Parallel data is not only a new data representation method, but also a new mechanism for data generalization and evolution. The dynamic trajectories of all data constitute a data dynamic system, and provide a new paradigm for data processing, representation, mining, and applications.
刘昕，王晓 ，张卫山， 汪建基，王飞跃. 平行数据:从大数据到数据智能*[J]. 模式识别与人工智能, 2017, 30(8): 673-681.
LIU Xin, WANG Xiao, ZHANG Weishan, WANG Jianji, WANG Feiyue. Parallel Data: From Big Data to Data Intelligence. , 2017, 30(8): 673-681.
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