Concerning the limited influence scope and high time complexity of existing influence maximization algorithms in social networks, a k-core filtered algorithm based on independent cascade model was proposed. Firstly, an existing influence maximization algorithm was introduced, its rank of nodes does not depend on the entire network. Secondly, pre-training was carried out to find the value of k which has the best optimization effect on existing algorithms but has no relation with the number of selected seeds. Finally, the nodes and edges that do not belong to the k-core subgraph were filtered by computing the k-core of the graph, then the existing influence maximization algorithms were applied on the k-core subgraph, thus reducing computational complexity. Several experiments were conducted on datasets with different scale to prove that the k-core filtered algorithm has different optimization effects on different influence maximization algorithms. After combined with k-core filtered algorithm, compared with the original Prefix excluding Maximum Influence Arborescence (PMIA) algorithm, the influence range is increased by 13.89% and the execution time is reduced by as much as 8.34%; compared with the original Core Covering Algorithm (CCA), the influence range has no obvious difference and the execution time is reduced by as much as 28.5%; compared with the original OutDegree algorithm, the influence range is increased by 21.81% and the execution time is reduced by as much as 26.96%; compared with the original Random algorithm, the influence range is increased by 71.99% and the execution time is reduced by as much as 24.21%. Furthermore, a new influence maximization algorithm named GIMS (General Influence Maximization in Social network) was proposed. Compared with PIMA and Influence Rank Influence Estimation (IRIE), it has wider influence range while still keeping execution time at second level. When it was combined with k-core filtered algorithm, the influence range and execution time do not have significant change. The experimiental results show that k-core filtered algorithm can effectively increase the influence ranges of existing algorithms and reduce their execution times; in addition, the proposed GIMS algorithm has wider influence range and better efficiency, and it is more robust.

Due to a large number of computational grids and slow convergence existed in the numerical simulation of Navier-Stokes (N-S) equation, Triangular mesh Spectral Finite Element Method based on area coordinate (TSFEM) was proposed. And further, TSFEM was paralleled with OpenMP. Spectral method was combined with finite element method, and the exponential function with infinite smoothness was selected as the basis function to replace the polynomial function in the traditional finite element method, which can efficiently reduce the amount of computational grids as well as improve the convergence and accuracy of the proposed algorithm. Because area coordinates can facilitate the calculation of triangular units, which were selected as the computing units to enhance the applicability of the algorithm. The lid-driven cavity flow was used to verify the TSFEM. The experimental results show that, compared with the traditional Finite Element Method (FEM), the TSFEM greatly improves the convergence rate and the calculation efficiency.

Concerning Hadoop Distributed File System (HDFS) data disaster recovery efficiency and small files, this paper presented an improved solution based on coding and the solution introduced a coding module of erasure GE to HDFS. Different from the multiple-replication strategy adopted by the original system, the module encoded files of HDFS into a great number of slices, and saved them dispersedly into the clusters of the storage system in distributed fashion. The research methods introduced the new concept of the slice, slice was classified and merged to save in the block and the secondary index of slice was established to solve the small files issue. In the case of cluster failure, the original data would be recovered via decoding by collecting any 70% of the slice, the method also introduced the dynamic replication strategies, through dynamically creating and deleting replications to keep the whole cluster in a good load-balancing status and settle the hotspot issues. The experiments on analogous clusters of storage system show the feasibility and advantages of new measures in proposed solution.