基于Seq2Seq模型的SparQL查询预测

SparQL Query Prediction Based on Seq2Seq Model

近年来,随着以数据为中心的应用大量增加,图数据模型逐渐被人们所关注,图数据库的发展也非常迅速,对于用户而言,往往更关心其在使用数据库过程中的效率问题.主要研究如何利用已有的信息进行图数据库的查询预测,从而进行数据的预加载与缓存,提高系统的响应效率.为了使得方法具有跨数据移植性,并深入挖掘数据间的联系,将SparQL查询提取为序列的形式,使用Seq2Seq模型对其进行数据分析和预测,并使用真实的数据集对方法进行测试,实验结果表明,本方案具有良好的效果.

In recent years,with the large increase in data-centric applications,graph data models have gradually attracted people’s attention,and the development of graph databases is also very rapid.Users are often more concerned about their efficiency in using databases.This work mainly studies how to use the existing information to query and predict the graph database,so as to preload and cache the data,and improve the response efficiency of the system.In order to make the method cross-data portable and dig deep into the connections between the data,this study extracted SparQL queries into the form of sequences,used the Seq2Seq model to analyze and predict its data,and tested the method using real data sets.Experiments show that the proposed scheme in this study has a sound effect.