Since fuzzy rough sets induced by fuzzy equivalence relations can not quite accurately reflect decision problems described by numerical attributes among fuzzy concept domain, a fuzzy rough set model based on neighborhood relation called NR-FRS was proposed. First of all, the definitions of the rough set model were presented. Based on properties of NR-FRS, a fuzzy neighborhood approximation space reasoning was carried out, and attribute dependency in characteristic subspace was also analyzed. Finally, feature selection algorithm based on NR-FRS was presented, and feature subsets was constructed next, which made fuzzy positive region greater than a specific threshold, thereby getting rid of redundant features and reserving attributes that have a strong capability in classification. Classification experiment was implemented on UCI standard data sets, which used Radial Basis Function (RBF) support vector machine as the classifier. The experimental results show that, compared with fast forward feature selection based on neighborhood rough set as well as Kernel Principal Component Analysis (KPCA), feature number of the subset obtained by NR-FRS model feature selection algorithm changes more smoothly and stably according to parameters. Meanwhile, average classification accuracy increases by 5.2% in the best case and varies stably according to parameters.

The performance requirements of application layer multicast are not identical in different business areas, and the network environment is more complex as follows: the multicast node is diversified, the communication channel is complex, the node scale is large, the amount of data is magnified and so on. The multicast programs should be optimized by analyzing the existing application layer multicast and combining new applications demands. By analyzing the evaluating indicator of application layer multicast, application layer multicast optimization method would be divided into the coding features optimization, the hierarchical clustering optimization, the node performance optimization, the optimal parent selection optimization and the routing information maintenance optimization. Through comparing the performance indicators of different types of optimization methods, the applicable environments were introduced separately, and further research directions were discussed finally.

Concerning the problem that Knowledge Fusion (KF) functions have no comprehensive functionalities, which has affected the validation rule customization, this paper presented a method of customizing Check-Mate validation rules based on the NXOpen secondary development technology. It gave a survey on the NXOpen application programming interfaces, introduced the method how to use NXOpen to implement validation logic, and how to use the programming languages, C++, C#, VB .NET, and Java respectively to build the program library of validation rules, and how to use KF to wrap the functions in the program library to get KF functions with a uniform interface. It built the validation rule classes using NXOpen secondary development technology, and calling the customization validation rules, and thereby promoted the capacity to build validation rule knowledge library, and helped to enhance the application of the product design standard.

Structural join operation is the main solution to Native XML database query. Based on the survey of existing structural join algorithms, a new structural join algorithm, depth partition based structural join algorithm (DRIAM) was proposed. In DRIAM, input data sets AList and DList were not necessary to be ordered or indexed so that the additional cost was avoided. AList and DList were not necessary to be loaded in the memory. Besides, the timecomplexity of DRIAM was very low.