The existing Influence Maximization (IM) algorithms of social network have the problem of low influence range caused by only selecting local optimal nodes at present. In order to solve the problem, considering the propagation advantage of core node and structure hole node, a maximization algorithm based on Structure Hole and Degree Discount (SHDD) was proposd. Firstly, the ideas of structure hole and centrality degree were integrated and applied to the influence maximization problem, and the factor α combining the structure hole node and the core node was found out to play the maximum propagation function, which made the information spread more widely to increase the influence of the whole network. Then, in order to highlight the advantages of the integration of two ideas, the influence of second-degree neighbor was added to the evaluation criteria of structure hole to select the structure hole node. The experimental results on data sets of different scales show that, compared with DegreeDiscount algorithm, SHDD can increase the influence range without consuming too much time, and compared with the Structure-based Greedy (SG) algorithm, SHDD can expand the influence range and reduce the time cost in the network with large clustering coefficient. The proposed SHDD algorithm can maximize the advantages of structure hole node and core node fusion when factor α is 0.6, and it can expand the influence range more steadily in the social network with large clustering coefficient.

To solve the problem of low query efficiency and high resource occupancy of the existing provenance graph query system, and consider the internal structure characteristics of provenance information, the relationship between the provenance of information and the data itself, a provenance graph query method based on double layer index structure was proposed. Firstly, for provenance graph query, a double layer index structure including global index based on dictionary table and local index based on bitmap was established. Global index was used to query the server nodes stored in provenance graph, and local index was for refining the query inside one server node. Secondly, based on the dual index structure, a provenance graph query method was designed, in view of the six kinds of selection index and three kinds of join link index. The experimental results show that the proposed method not only improves the query efficiency, but also reduces the waste of memory resources.

Aiming at the problem of data sparsity, poor portability and lack of recognition of multiple words (more than three words) in micro-blog new word discovery algorithm, a new word discovery algorithm based on improved Mutual Information (MI) and Branch Entropy (BE), named MBN-Gram, was proposed. Firstly, the N-Gram was used to extract the candidate terms of new words, and the rules of using frequency and stop words were used to filter the candidates. Then the improved MI and BE were used to expand and filter the candidates again. Finally, the corresponding dictionary was used to screen, so as to get new words. Theoretical and experimental analysis show that the accuracy rate, recall rate and F value of MBN-Gram algorithm were improved. Experimental results shows that the MBN-Gram algorithm is effective and feasible.

There exists the problem of security insufficience among the data privacy protecting technology which is the privacy leakage caused by homogeneity and background knowledge attack when computing equivalence classes in the anonymity process. To solve the problem, an anonymized data privacy protection method based on differential privacy was put forward, and its model was constructed. ε-MDAV (Maximum Distance to Average Vector) algorithm was presented, in which micro-aggregation MDAV algorithm was used to partition similar equivalence classes, and SuLQ frame framework was introduced into the anonymous attribute process. Laplace mechanism was used to reasonably control the privacy protection budget. The comparison of availability and security under different privacy protect budgets verifies that the proposed method effectively improve data security while guaranteeing high data availability.

With the widespread application of the data flow, the abnormal data detection problem in data flow has caused more attention. Existing Kalman filtering algorithms need small amount of historical data, but they only apply to single abnormal point detection. The effect to complex continuous outlier points is poor. In order to solve the problem, a Kalman filtering algorithm based on multidimensional impact factors was proposed. The algorithm joined the three dimensions of impact factor as space, time, provenance as well. In case of different weather and flood season, the algorithm adjusted the controlling parameters of system model parameters, and got a more accurate estimate of measurement noise. The detection accuracy of the algorithm could be improved significantly. The experimental results show that under the premise of guaranteeing similar running time, the detection error rate of this algorithm is far lower than Amnesic Kalman Filtering (AKF) and Wavelet Kalman Filtering (WKF) algorithms.