基于数据挖掘的数据库语言查询过程分析

Query Process Analysis of Database Language Based on Data Mining

针对现有数据库语言查询过程分析方法由于切分法不完善,导致查询语句存在着查询准确率低的问题,为此提出基于数据挖掘的数据库语言查询过程分析方法研究。将以查询语句分词数组为基础,通过数据库语义分析识别查询目标与查询条件,构建语义依存树,划分为集合块,并通过综合转换算法将自然语言查询转换为SQL语句。在此基础上,利用数据挖掘技术在数据库中定位用户需求数据,提取定位数据并反馈给用户,实现数据库语言查询过程的分析。实验结果显示:针对复杂查询语句来说,所提方法的查询目标与条件识别系数、SQL语句转换系数较大,表明该方法的查询准确率高,能够为用户提供更加便捷、准确的数据库服务。

In view of the problem of low query accuracy of the query process analysis methods of representative database language due to the wrong segmentation of lexical,this paper proposes a method of database language query process analysis based on data mining.We analyze the structure of natural language query statements according to the characteristics of natural language query statements,and use the maximum matching method to segment query statements.Based on the query sentence segmentation array,the query objectives and query conditions are identified through database semantic analysis,a semantic dependency tree is built and divided into set blocks,and then the comprehensive transformation algorithm is used to convert query natural language queries to SQL statements.On this basis,the data mining technology is used to locate the user demand data in the database,the location data are extracted and feedbacked to the user,the analysis of the database language query process is completed.The experimental results show that for complex query statements,the query target and condition identification coefficient and SQL statement conversion coefficient of the method proposed in this paper are large,which shows that the method has high query accuracy and can provide users with more convenient and accurate database services.