面向动态大数据的粒矩阵多维关联性分析

The ever-increasing Big Data is acclaimed, and the key point of Big Data is data analysis. However focusing on the Big Data with dynamic and multiple-dimensional characteristic is difficult to obtain reliable and accurate analytical results by the traditional data analysis methods. Therefore this is an important opportunity and great challenge for the data analysis methods to be developed. This project aims to make an important research and investigation of the multiple correlation analysis for dynamic Big Data. The research object is dynamic Big Data, and the.research is based on the theory of Granular Computing (GrC). We get the theoretical thought of Granular Matrix completely new in this project. The first step is to build the Granular Matrix for dynamic Big Data. The second step is thatwe will make research on the logical reduction operation of Granular Matrix. The third goal is build the multiple correlation analysis models based on granular matrix for dynamic Big Data. The project is expected to reveal the multiple correlation analysis for dynamic Big Data. On one hand this project research achievements would provide a scientific basis for multiple correlation analysis and revelation of the objective law in Big Data area. On the other hand it is also an important implication for sustainable development of Big Data.

当前日益增长的大数据倍受亲睐，大数据的核心则是数据分析。然而聚焦大数据的动态、多维特性，传统数据分析方法难以取得可靠准确的分析结果，数据分析方法面临着重要的发展机遇和严峻的挑战。本项目针对动态大数据的多维关联性分析问题进行研究和探讨，以动态大数据为研究对象，采用粒计算理论为研究基础并提出粒矩阵思想，研究构建面向动态大数据的粒矩阵方法，分析粒矩阵的逻辑约简运算，确定了基于粒矩阵的动态大数据多维关联性分析模型。项目预期将揭示动态大数据的多维关联性分析方法，实现基于粒矩阵的多维关联性分析，建立动态大数据的多维关联性分析系统。项目研究成果为高效利用动态大数据进行多维关联性分析和揭示数据隐含的客观规律提供了科学依据；同时对大数据的可持续发展具有重要意义。

当前日益增长的大数据倍受亲睐，大数据的核心则是数据分析。然而聚焦大数据的动态、多维特性，传统数据分析方法难以取得可靠准确的分析结果，数据分析方法面临着重要的发展机遇和严峻的挑战。针对动态大数据的多维关联性分析问题进行研究和探讨，以动态大数据为研究对象，采用粒计(Granular Computing, GrC)理论为研究基础并提出粒矩阵思想，研究构建面向动态大数据的粒矩阵方法，分析粒矩阵的逻辑约简运算，确定了基于粒矩阵的动态大数据多维关联性分析模型。旨在为高效利用动态大数据进行多维关联性分析和揭示数据隐含的客观规律提供了科学依据；同时对大数据的可持续发展具有重要意义。