基于群体智能的半结构化数据查询优化算法  

Semi-Structured Data Query Optimization Algorithm Based on Swarm Intelligence

作　　者：高俊杰[1] 杨帆 GAO Jun-jie;YANG Fan(School of Computer&Information Technology,Shanxi University,TaiyuanShanxi030006,China;Department of Computer Teaching,Shanxi Medical University,TaiyuanShanxi030001,China)

机构地区：[1]山西大学计算机与信息技术学院,山西太原030006 [2]山西医科大学计算机教学部,山西太原030001

出　　处：《计算机仿真》2021年第8期381-385,共5页Computer Simulation

基　　金：国家自然科学基金重点项目(61936012):基于语言认知机理的汉语框架语义计算研究。

摘　　要：传统算法不能有效结合半结构化数据特征,在进行算法运行过程中所查询的数据量较少,且时间较长。于是基于群体智能研究了一种新的半结构化数据查询算法。采用粒子群优化算法建立半结构化数据查询模型,标记空间内的数据特征,运用中心-离散算法对计算模型数据中的不同粒子类型进行查询,在不同范围内实现对数据的查询与搜索。采用映射方法形成数据查询模型集合,并利用映射关系与有向图中的数据内容建设半结构化数据模型储存空间,利用标签树区分数据结构与数据层次,实现对半结构化数据的查询优化。实验结果表明,所提算法的数据查询量更多,查询时间更短,说明上述方法优化能力更强。Traditional algorithms can not effectively combine the characteristics of semi-structured data,so the amount of data queried in the process of algorithm running is less and the time is longer.In this regard,we studied a new semi-structured data query algorithm based on swarm intelligence.First of all,particle swarm optimization algorithm was used to found semi-structured data query model and label the data features in the space.Secondly,the center discrete algorithm was applied to query different particle types in the computational model data,realizing the query and search of data in different ranges.Then,based on the mapping method,the data query model set was established,and the semi-structured data model storage space was constructed by using the mapping relationship and the data content in the directed graph.Eventually,the tag tree was used to distinguish the data structure and data hierarchy to realize the query optimization of semi-structured data.The experimental results show that the algorithm has more data queries and shorter query time,indicating that the optimization ability of the method is stronger.

关 键 词：群体智能 数据查询 粒子群体优化算法 半结构化模型 标签树 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]