时序大数据的广泛存在迫切需要有效的分析和挖掘方法，以发现其中有价值的信息和知识。然而，规模大、维数高、属性种类多（数值属性、非数值属性）等特性使得已有的时序数据挖掘算法表现低效或无效，从而无法适应现实的需要，探求新的时序大数据分析和挖掘方法成为当务之急。人脑在分析和处理时序数据时，不仅能根据问题需要对信息适度粒化进而在粒子层面对数据进行分析，还能根据需要适时合理地将从已有数据中得到的知识运用到后续数据的分析中去。人脑的这两种处理信息机制既灵活又高效。将这两种机制引入到时序大数据的分析中去，有可能导致科学有效的数据分析方法的产生。本项目旨在探究将这两种机制引入到时序大数据的分析中去的方法，并将所得到的新方法应用到现实的时序大数据分析问题，在验证新方法的同时，完善新方法，建立一套基于知识指导和模糊信息粒化的时序大数据分析和挖掘方法。

Temporal big data widely exist in urgent need of effective data analysis and mining methods for finding valuable information and knowledge in it. However, such characteristics as Large-scale, high-dimension, multi-kind attributes(real-valued atrributes and nonreal-valued attributes) and so on lead to the lower efficiency or invalidation of the existing mining algorithms of temporal data. Searching for new kind of analysis methods and mining algorithms of temporal big data now becomes the urgent matter. Human brains can not only do moderate information granulation according to the needs, and in turn, at the granule level to analyze the data, but also apply the knowledge obtained from old data to the analysis of new data timely and properly according to the needs. These two mechanisms of human brains in information processing manifest high flexibility and efficiency. Introducing these two mechanisms to the analysis and mining of temporal big data, could bring us scientific and effective data analysis methods. This project aims to explore the new methods for introducing these two mechanisms into the analysis of temporal big data, and apply the new methods to the analysis of temporal big data in real problems. After the process of validating and improving these new methods, a set of analysis and mining methods based on knowledge-guidance and fuzzy information granulation will be established for temporal big data.