Considering the data security problems of service sharing in SWIM (System Wide Information Management), the risks in the SWIM business process were analyzed, and a malicious data filtering method based on Latent Dirichlet Allocation (LDA) topic model and content mining was proposed. Firstly, big data analysis was performed on four kinds of SWIM business data, then LDA model was used for feature extraction of business data to realize content mining. Finally, the pattern string was searched in the main string by using KMP (Knuth-Morris-Pratt) matching algorithm to detect SWIM business data containing malicious keywords. The proposed method was tested in the Linux kernel. The experimental results show that the proposed method can effectively mine the content of SWIM business data and has better detection performance than other methods.

To help users to quickly retrieve the interesting game strategies, a knowledge driven automatic annotating algorithm for game strategies was proposed. In the proposed algorithm, the game domain knowledge base was built automatically by fusing multiple sites that provide information for each game. By using the game domain vocabulary discovering algorithm and decision tree classification model, game terms of the game strategies were extracted. Since most terms existing in the strategies in the form of abbreviation, the game terms were finally linked to knowledge base to generate the full name semantic tags for them. The experimental results on many games show that the precision of the proposed game strategy annotating method is as high as 90%. Moreover, the game domain vocabulary discovering algorithm has a better result compared with the n-gram language model.

To solve the problem of small fault detection of instruments in process industry, independent components were extracted by Independent Component Analysis (ICA) from instruments recorded data. And independent component subspaces were established according to the contribution matrix. Fault detection model was constructed in each independent component subspace with statistical variables. A proper ensemble strategy was chosen by combining all the fault detection results. Finally, the instrument with fault was located by contribution algorithm. The simulation results with TE (Tennessee Eastman) process show that this method has higher precision on small fault detection and more flexibility with proper ensemble strategy.

Concerning the premature convergence of invasive weed optimization algorithm, a new invasive weed optimization with multi-population based on chaotic sequence (CMIWO) was proposed. Firstly, chaotic sequence was adopted to initialize population at the initialization of algorithm, which improved the quality of the initial solution. Secondly, threshold was used to estimate the cluster degree of individuals in iterations and if cluster degree was less than threshold, initializing population with chaotic sequence was implemented again, thus the algorithm could effectively jump out of local minima. Thirdly, the weed population was divided into five groups to collaborate so as to discourage premature convergence, thus improving the algorithm's precision and increasing the convergence speed. In the end, the test results on eight test functions show that the proposed algorithm improves the accuracy by 25% to 300% than basic algorithm in terms of optimal value and 50% to 100% for standard deviation.