Spatial-temporal co-occurrence patterns refer to the video object combinations with spatial-temporal correlations. In order to mine the spatial-temporal co-occurrence patterns meeting the query conditions from a huge volume of video data quickly， a spatial-temporal co-occurrence pattern mining algorithm with a triple-pruning matching strategy — Multi-Pruning Algorithm （MPA） was proposed. Firstly， the video objects were extracted in a structured way by the existing video object detection and tracking models. Secondly， the repeated occurred video objects extracted from a sequence of frames were stored and compressed， and an index of the objects was created. Finally， a spatial-temporal co-occurrence pattern mining algorithm based on the prefix tree was proposed to discover the spatial-temporal co-occurrence patterns that meet query conditions. Experimental results on real and synthetic datasets show that the proposed algorithm improves the efficiency by about 30% compared with Brute Force Algorithm （BFA）， and the greater the data volume， the more obvious the efficiency improvement. Therefore， the proposed algorithm can discover the spatial-temporal co-occurrence patterns satisfying the query conditions from a large volume of video data quickly.

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.

A concurrent processes grouping output method was proposed to address the problem that storage performance decreases when testing Nucleus for European Modeling of the Ocean (NEMO) system with massive processes on TH-1A supercomputer. This method designed a reasonable control strategy based on TH-1A architecture and split processes into groups to alleviate the process competition of concurrency I/O. Testing result shows that the storage performance of the GYRE012 global sample can be improved more than 33% using the optimization method of concurrent processes grouping output, and in the mean time the total performance can be improved about 28%.