Concerning the low recommendation accuracy of personalized commodity recommendation methods, a Commodity Recommendation Method Integrating User Trust and Brand Recognition (TBCRMI) was proposed. By analyzing the user's purchase behavior and evaluation behavior, the user's recognition of brands and the activities of users themselves were calculated. Then Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm was used to cluster the users, based on which the user trust relationship was fused, and the nearest neighbors were obtained by Top- K method. Finally, the target user commodity recommendation list was generated based on the nearest neighbors. In order to verify the effectiveness of the algorithm, two datasets (Amazon Food and Unlocked Mobile Phone) were used, User based Collaborative Filtering (UserCF) algorithm, Collaborative Filtering recommendation algorithm with User trust (SPTUserCF) and Merging Trust in Collaborative Filtering (MTUserCF) algorithm were chosen, and the accuracy, recall and F1 value were compared and analyzed. The experimental results show that TBCRMI is superior to the commonly used personalized commodity recommendation methods in either multi-brand comprehensive recommendation or single brand recommendation.

The existing (fully) homomorphic encryption schemes fail to meet practical needs for poor efficiency. To explore new resolution for better homomorphic encryption schemes, the possibility to construct homomorphism on a public key encryption scheme in literature based on Chinese Residue Theorem (CRT) was studied. The possibility of the original scheme to construct the addition and multiplication homomorphic operations was investigated. The original scheme was proved to be unsuitable for constructing homomorphic addition and multiplication operations. Several problems concerning security and efficiency existing in the original scheme were analyzed. Then a revised scheme with tougher security under proper configurations was given, as well as its correctness verification. After that, analysis on security and computing complexity of the revised scheme was given, emphasizing on its ability against the lattice reduction attack. Afterwards, the feasibility of building homomorphic operations on the revised scheme was studied and the main performance comparison between the original and the revised schemes was constructed. Finally, experience on building homomorphism was summarized and some advice on constructing an ideal (fully) homomorphic encryption scheme was presented.

In mass data era, the research on knowledge discovery of massive and distributed data has become the hot spot in both academic field and industry. The problem of load balance is one of the important factors that must be considered in developing a distributed mining algorithm. Therefore, a distributed association rules mining algorithm with load balance based on vertical FP-tree (VFP-LBDM) was proposed in this paper. Vertical frequent pattern tree was used in this algorithm to store items and their associations, and there was no need to combine the local mining results. Therefore, the communication cost was reduced and the processing procedure was also simplified. At the same time, the algorithm used the hybrid architecture in which the central site assigned tasks according to the processing capacity of each local site. It realized the load balance and improved the performance of the algorithm. The experiment shows that the algorithm given in this paper is feasible and has higher efficiency.

For the purpose of events extraction from large-scale short posts of microblogging service, a complete event detection and tracking algorithm was proposed using cloud framework. First, based on the number of forward and comment of the microblog, the posts were expressed as Vector Space Model (VSM). Then the keywords were extracted using RIHDBSCAN (Incremental Hierarchical DBSCAN based on Representative posts) to realize the event detection and tracking. Considering that a single node cannot quickly and efficiently handle the large amount of data, the algorithm would be deployed on Hadoop, a cloud computing platform. The experiment on real microblog data extracted from Sina microblogging platform shows that the proposed method achieves higher performance than that of TF-IDF (Term Frequency-Inverse Document Frequency) and UF-ITUF (User Frequency-Inverse Thread User Frequency), and the use of cloud framework improves the processing speed. Therefore, it is suitable for data analysis and mining on huge datasets.

The kernel function methods can discover the nonlinear distribution rules among the images of high precision prints. And the mining capacity is decided by the kernel function and its parameters. Selecting the kernel function is imminent to the development and application in kernel function theory. Based on the intelligent detection of prints, a new learning kernel method based on the optimization was presented for the industry of high precision printing to make the kernel function method to achieve optimal performance. Unlike the traditional calculation method, the kernel's parameter was continuously changing in kernel space, which meant that the learning scope expanded one dimension. The experimental results show that the iterative algorithm based on the theoretical analysis only needs ten iterations to get the statistical optimal kernel function and its parameters, and the recovery error of the kernel function is statistically minimum.

Collaborative recommendation is widely used in E-commerce personalized service. But the existing methods cannot provide high level personalized service due to sparse data and cold start. To improve the accuracy of collaborative recommendation, a collaborative recommendation method based on Social Network Analysis (SNA) was proposed in this paper by using SNA to improve the collaborative recommendation methods. The proposed method used SNA technology to analyze the trust relationships between users, then quantified the relationships as trust values to fill the user-item matrix, and used these trust values to calculate the similarity of users. The effectiveness of the proposed method was verified by the experimental analysis. Using trust values to expand the user-item matrix can not only solve the problem of sparse data and cold start effectively, but also improve the accuracy of collaborative recommendation.

To solve the problems such as low level automation and poor adaptability of current entity recognition methods, a Deep Web entity recognition method based on Back Propagation (BP) neural network was proposed in this paper. The method divided the entities into blocks first, then used the similarity of semantic blocks as the input of BP neural network, lastly obtained a correct entity recognition model by training which was based on the autonomic learning ability of BP neural network. It can achieve entity recognition automation in heterogeneous data sources. The experimental results show that the application of the method can not only reduce manual interventions, but also improve the efficiency and the accuracy rate of entity recognition.

To resolve the peculiar problem of the Jacobian matrix for a special class of nonlinear equations, an improved Newton mtheod was proposed based on the dual space. This paper proposed an explicit formula to compute the dual space of an ideal in a point through polynomial multiplication, and constructed augmented equations using the dual space. Meanwhile, the Jacobian matrix of augmented equations at initial point was full rank, and then the algorithm recovered quadratical convergence of Newton's iteration. The experimental results show that after three iterations, the accuracy of computation can achieve 10^(-15). The proposed method further enriches the theories of the dual space of ideal in algebra geometry and provides a new method for the numerical calculation in engineering applications.

Query interface schema matching is a key step in Deep Web data integration. Dual Correlated Mining (DCM) is able to make full use of association mining method to solve the problems of complex interface schema matching. There are some problems about DCM, such as inefficiency and inaccuracy in matching. Therefore, a new method based on matching degree and semantic similarity was presented in this paper to solve the problems. Firstly, the method used correlation matrix to save the association relationship among attributes; and then, matching degree was applied to calculate the degree of correlation between attributes; at last, semantic similarity was used to ensure the accuracy of final results. The experimental results on BAMM data sets of University of Illinois show that the proposed method has higher precision and efficiency than DCM and improved DCM, and indicate that the method can deal with the query interface schema matching problems very well.

The traditional distance-based similarity calculation method was described. Concerning that the method of distance calculation does not contain sufficient semantic information, this paper proposed an improved method which used WordNet and edge weighting information between the concepts to measure the similarity. It considered the level of depth and density of concepts in corpus, i.e. the semantic richness of concept. Using this method, the authors can solve the semantic similarity calculation issues and make the calculation of similarity among concepts easy. The experimental results show that, the proposed method has a 0.9109 correlation with the benchmark data set-Rubenstein concept pairs. Compared with the classical method, the proposed method has higher accuracy.

In Web service matching, the concept similarity computation based on semantic distance plays an important role. Because the influence of semantic asymmetry and semantic density has not been considered in current concept similarity computation based on semantic distance, the computation result is not accurate. To enhance the accuracy of the concept similarity computation, the semantic distance-based concept similarity computation was improved by adding the asymmetry factor and density factor on the basis of the current research. Finally, the feasibility of the improved semantic distance-based concept similarity computation was verified via an example. According to the contrast analysis, the improved semantic distance-based concept similarity computation can reflect semantic relationship between concepts more truly and the computation result is more in line with the objective reality.

A great deal of Web services and requests exist in Web environment. Web services matchmaking based on semantic can improve accuracy of service discovery. Because of complicated semantic calculation, the reaction rate of Web service matchmaking was slow. Firstly, this paper analyzed the process of semantic Web service matchmaking to make clear that the large amount of semantic calculation exited in Inputs/Ouputs (I/O) matchmaking phase. Secondly, an I/O matchmaking optimized method of semantic Web services with efficient index was put forward on the basis of the studies on I/O matchmaking algorithms and main influence factors of semantic similarity, which included the creation of efficient index and the raise of the heuristic filter mechanism based on the re-hash secondary detection. Finally, the proposed method was proved to be feasible and rational via an instance. The proposed method can reduce semantic calculation and promote reaction rate by filtering some irrelevant Web services. Furthermore, the experience of users can be improved.

A gossip-based adaptive membership protocol which is oriented to dynamic heterogeneous P2P was put forward. This protocol could dynamically adjust node degree according to node capability, and thus the node degree could be matched with the node capability, and then increasing the resource utilization and load balance. The basic operations of the protocol include: node joining, node exit, node failure restore, and node capability aggregation. The experimental results show that the proposed protocol which adapted to the node capability has higher resource utilization than the not-adapted.