平行数据:从大数据到数据智能

Parallel Data: From Big Data to Data Intelligence

为了解决实际问题,大数据分析处理系统需要获取数据,然而实际场景中收集到的实际数据通常不完备.另外,大多数问题的解决方案通常是由问题引导或者仅仅进行数据分析,运行参数调整和设定带有较大的盲目性,难以达到应用的智能性.为此,文中提出平行数据的概念和框架,根据实际数据经计算实验产生真正的虚拟大数据,结合默顿定律,以期待的解决方案与问题进行广义对偶,引导大数据聚焦到实际问题.实际数据与虚拟数据动态互动,平行演化,形成一个虚实相生、数据动态变化的过程,最终使数据具备智能,进而解决未知的问题.平行数据不但是一种数据表示形式,更是一种数据演化机制与方式,其特色是虚实互动,所有数据的动力学轨迹构成了数据动力学系统.平行数据为数据处理、表示、挖掘和应用提供了一个新的范式.

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.