Focusing on the difficulty in monitoring and judging the credibility of user behaviors in online applications and the problem of weak theorey of user behavior credibility analysis, a credibility analysis method of online user behavior was proposed based on non-interference theory. Firstly, the static credibility of single behavior was defined from three aspects-the behavioral entity identity, state and environment of the single behavior, and the static credibility verification strategy was given. Thereafter, dynamic behavioral credibility was defined from the perspectives of execution process and result, and dynamic credibility verification strategy was given. Finally, the user behavior process was constructed based on the single behavior, and the credibility determination theorem of user behavior process was proposed based on the idea of credibility extension, and the theorem was proved by using non-interference theory. The correctness and validity of the proposed method were verified by the provement process and result.

High performance analysis is a major topic in science computation and large-scale software system.Using a better data sampling method will gain better performance analysis.Traditional performance data are sampled by operation system based on rough granularity source code level,such as process time,memory usage percentage,etc.These data cannot show up hypostasis of software and provide a better effective proposition for optimization.This paper introduced a much more precise sampling method using CPU hardware performance counters(CHPC) to provide CPU data like instruction cycles,cache misses,branch prediction,and so on,and given detail scene of software status.With CHPC data,more accuracy software performance analysis and compiler optimization can be reached.

For a real database application system with large numbers of materialized views, view merging was introduced to decrease the number of total views so as to reduce the search space. A merging tree was described and an efficient merging algorithm was presented based on such tree. Experiment results show that view merging is an efficient approach to fast search those materialized views that may be used to answer a query, and can improve the performance of query processing observably.