基于自适应特征映射的云数据管理索引算法

Cloud Data Management Index Algorithm Based on Adaptive Feature Mapping

随着Web数据库技术的发展,需要对云数据进行索引管理,提高Web数据库访问能力。传统方法采用高斯边缘化路径控制方法进行索引,在无法及时获知Web云数据的先验语义特征信息时,索引精度不高。提出一种基于自适应特征映射的云数据管理索引算法。构建云数据管理模型,提取云数据自适应特征,采用语义相似度特征分析方法实现特征映射,采用一维搜索方法进行峰值搜索,初始化数据库索引起始时间点,设置语义高斯边缘化索引复激活函数,对本层链路和跨层链路进行均衡处理,达到云数据管理和优化索引的目的。仿真结果表明,采用该算法能有效提高云数据管理索引精度,提高云数据的准确调度和访问能力。

With the development of Web database technology, the need for the index management of cloud data, improve the ability to access the Web database. The traditional method using Gaussian edge path control methods for indexing, it cannot be promptly informed of the cloud web data a priori semantic feature information, the retrieval accuracy is not high. We pro-pose a new cloud data management index algorithm based on feature mapping. Building cloud data management model, cloud data adaptive feature extraction, using semantic similarity analysis method to realize the feature mapping, peak search using a one-dimensional search method, database initialization cable caused the initial time point, setting semantic Gaussian edge indexed complex activation functions, of the link layer and cross link layer of a balanced approach to cloud data management and optimization of the index. Simulation results show that using this algorithm can greatly improve the precision of the cloud data management index and improve the cloud data accurate scheduling and access.