Aiming at the low data-transmitting efficiency of the traditional message-oriented middleware in power information system, a firm real-time data-transmitting system based on data stream-transmitting mechanism was proposed. Queue caching mechanism was adopted to realize the asynchronous sending and batch confirmation of message. Data stream-transmitting mechanism was designed to eliminate the cache latency and the cost of cache resources of the data on transit node to improve the timeliness and concurrency of data transmission. Distributed and data routing thought was used-data to make the node network to the third-party system transparently and achieve a data routing distribution function. The simulation results of a provincial electric power information system data exchange scene, verified the system performance. Concurrent data exchange capacity is 3000 concurrent. Transmission speed in the gigabit bandwidth system environment is 980 MB/s. Switching delay is kept in milliseconds.

To improve the accuracy of recommended Web resources, a personalized recommendation algorithm based on ontology, named BO-RM, was proposed. Subject extraction and similarity measurement methods were designed, and ontology semantic was used to cluster Web resources. With a user's browser tracks captured, the tendency of preferences and recommendation were adjusted dynamically. Comparison experiments with collaborative filtering algorithm based on situation named CFR-RM and personalized prediction algorithm based on model were given. The results show that BO-RM has relatively stable overhead time and good performance in Mean Reciprocal Rank (MRR) and Mean Average Precision (MAP). The results prove that BO-RM improves the efficiency by using offline data analysis for large Web resources, thus it is practical. In addition, BO-RM captures the users' interest in real-time to updates the recommendation list dynamically, which meets the real needs of users.

In numerous classification methods, although Naive Bayesian (NB) classification algorithm is simple and effective, its attribute independence assumption ignores the correlation among attributes. To consider the influence of the attribute independence assumption, a new grouping technology which clusters the conditional attributes was proposed. This technology not only overcomes the deficiency arising from the attribute independence assumption of the traditional NB classification algorithm, but also reflects the different correlation intensity among attributes when the classification is different. Simulation results on a variety of UCI data sets illustrate the efficiency of this method.