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This paper establishes an intelligent big data storage system IHDFS, based on the existing open source distributed file storage system HDFS. The system proposes and implements big data de-duplication module, big data placement module, big data intelligent migration module, and big data encoding module, which improves the efficiency of user visits and saves the storage space of the cluster. Experimental results show that the data de-duplication module can save the storage space. The data placement module provides a reasonable distribution of file upload storage layer, which twice the uploading speed; the data intelligent migration module improves the hit rate of files on the upper storage layer, which improves the efficiency of obtaining data; the data encoding module saves the storage space of the cluster about one third of the original. [ABSTRACT FROM AUTHOR]

This paper proposes a searchable encryption model for graph data to address the problem that most of the current searchable encryption schemes cannot deal with complex data structures. The algorithms and security models of the scheme are described. Taking the encryption index of matrix structure into account， the detailed description of the searchable encryption scheme for graph data that supports adjacency queries is presented. The scheme utilizes pseudo-random functions and pseudo-random permutations to protect the privacy of the graph data and the encrypted index. Real models and ideal models test the security of the scheme. Compared with traditional searchable encryption schemes， the proposed scheme has more flexible searching ability and higher efficiency， which promotes broader application under big data environment. [ABSTRACT FROM AUTHOR]

Under the background of big data， there exist data uncertainties due to privacy protection， data loss and so on. In data stream system， data arrive at continuously and cannot be obtained all. In addition， all the inforation cannot be aquired with only one scan. Therefore， an incremental classification model is constructed to deal with uncertain data stream classification. The weighted Bayes based on VFDT (very fast decision tree) for uncertain data stream—WBVFDTu on the basis of VFDT algorithm is presented in the paper. The uncertain information can be analysed quickly and effectively in both the learning stage and classification stage. In the learning stage， a decision tree model for uncertain data stream is quickly constructed by using Hoeffding bound theory. In the classification stage， the weighted Bayes classifier in the tree leaves is used to improve the performance of the classification. Experimental results show that the proposed algorithm can very quickly learn uncertain data stream and improve the classification performance of the model. [ABSTRACT FROM AUTHOR]