摘要
大型关系数据库变更轨迹信息快速捕获,能够提高数据库安全性能。对数据库变更轨迹信息进行捕获时,需要计算出最优个体,对变更信息属性进行度量分析,完成变更轨迹信息捕获。传统方法将应用层中的访问控制层次划分出来,提出新的变更信息捕获方法,但忽略了对变更信息属性进行度量分析,导致捕获结果利用率低,提出基于遗传算法的大型关系数据库变更轨迹信息快速捕获方法。利用势能函数定义变更轨迹信息属性之间相异性的度量测度,据此对信息间距离进行计算,获得变更信息属性度量函数值。基于遗传算法,通过轮赌选择法计算个体选取概率并获取当前最优个体,形成新群体。利用选取概率计算适应度函数,根据适应度函数获取变更信息的属性捕获结果。实验表明,上述方法信息捕获过程中安全性系数高,信息捕获结果有较高利用率。
Traditional method neglects to measure and analyze the attribute of changed information, leading to low utilization rate of capture result. In this article, we focus on a method to fast capture the changed track information in large relational database based on genetic algorithm. The potential energy function was used to define the metric measure of dissimilarity between attributes of changed track information. On this basis, the distance between information was calculated to obtain the function value of changed information attribute measurement. Based on genetic algorithm ,the selection probability of individual was calculated through the method of roulette selection, and then current optimal individual was obtained to form a new population. Finally, we used the selection probability to calculate fitness function. Thus, we obtained the attribute capture result of changed information based on the fitness function. Simulations show that the security coefficient of proposed method is high in the process of information capture and the utilization rate of information capture result is high.
作者
徐承俊
XU Cheng-jun(Yingtan Campus of Jiangxi Normal University,Jiangxi Yingtan 335000,China)
出处
《计算机仿真》
北大核心
2019年第7期292-295,共4页
Computer Simulation
关键词
关系数据库
变更轨迹
信息
捕获
Relational database
Change track
Information
Capture
