校园大型点云数据库的优化挖掘算法分析

Optimization of Campus Large Point Cloud Database Mining Algorithm

针对传统方法应用于大型校园数字图书馆中数据查询服务时的不足,提出了一种基于云计算的数据查询方法。算法首先运用云计算技术找到数据库中的查询请求的目标节点,然后通过把单个基因位的理论引入到传统的遗传云计算算法中,定义服务节点的相似节点集和等价节点集来进行二次搜索,将各个基因位取得的优秀个体重新组合成一个新的个体,利用遗传过程中出现过的任何优秀的基因,而不仅仅是针对优秀个体,即便是一个适应度很差的个体,也可能有某段基因的适应度在同位置的基因中有着优秀的适应度,保证数据库查询最优解的准确。实验结果表明,算法在查询的数据质量、服务节点的负载能力以及查询的效率方法要优于传统的方法。

Cloud computing and its application is a hot spot in current research, in view of the traditional methods used in the large-scale campus digital library data query service, puts forward a method of data query based on cloud computing. First use cloud computing technology to find the method in the database query requests the target node, and then by a single gene, the level of theory is introduced into the traditional genetic algorithm in cloud computing, there may be a pe-riod of gene of fitness in the same position has a good fitness, guarantee the accuracy of the optimum solution of the database query. Experimental results show that the method in query of data quality, service node load capacity and the efficiency of the query method is superior to the traditional way.