关键词: 皮尔森相关算法, 云存储, 层次化, 冗余信息, 去冗优化

Abstract: Aiming at the problems of redundancy elimination efficiency and low recall rate in traditional cloud storage, a hierarchical redundancy elimination optimization method based on Pearson correlation algorithm is proposed. According to the attribute distribution similarity measure value of redundant information in the
hierarchical structure, the distance matrix of redundant information is constructed to classify the hierarchical redundant information by calculating the similarity between redundant information. By analyzing the structure of different types of redundant information, using data dimension reduction constraints and central limit principle,the objective function of redundant information feature space compression is constructed, and the hierarchical redundant information features are extracted based on the redundancy optimization hyperplane, the distance between redundant information sample points and positive and negative hyperplanes is calculated. The fuzzy factor is defined by Pearson correlation algorithm, and then the feature effectiveness in the hierarchical structure of cloud storage is defined. The effectiveness function of redundant information features is constructed, and the redundancy optimization of cloud storage hierarchy is realized. Experimental results show that the design method improves the redundancy removal efficiency and has good performance in recall rate.

Key words: Pearson correlation algorithm, cloud storage, hierarchical, redundant information, eliminate redundant optimization