计算机工程2024，Vol.50Issue(1)：30-38,9.DOI:10.19678/j.issn.1000-3428.0066743
健壮且自适应的学习型近似查询处理方法研究
Research on Robust and Adaptive Learned Approximate Query-Processing Method
摘要
Abstract
Owing to the significant latency of exact queries on large-scale datasets,Approximate Query-Processing(AQP)techniques are typically applied to online analytical processing to return query results within interactive timescales with minimal error.The existing learning-based AQP methods decouple the underlying data and convert I/O-intensive calculations into CPU-intensive calculations.However,because of the limitations of computing resources,model training is typically performed based on random data samples.Such training data eliminate rare populations,thus resulting in unsatisfactory prediction accuracy by the model.Hence,this paper proposes a Stratified Sampling-based Sum-Product Network(SSSPN)model and designs an AQP framework based on the abovementioned model.Stratified samples can effectively avoid the elimination of rare populations and significantly improves the model accuracy.Additionally,in terms of dynamic data updates,this paper proposes an adaptive model-update strategy that allows the model to detect data shifts timely and automatically perform updates adaptively.Experimental results show that compared with the performance of AQP methods based on sampling and machine learning,the average relative errors of this model on real and synthetic datasets are approximately 18.3%and 2.2%lower,respectively;in scenarios where data are dynamically updated,both the accuracy and query latency of the model are favorable.关键词
近似查询处理/和积网络/分层抽样/数据偏移/自适应更新Key words
Approximate Query-Processing(AQP)/Sum-Product Networks(SPN)/stratified sampling/data shift/adaptive update分类
信息技术与安全科学引用本文复制引用
乔艺萌,荆一楠,张寒冰..健壮且自适应的学习型近似查询处理方法研究[J].计算机工程,2024,50(1):30-38,9.基金项目
国家自然科学基金(62072113). （62072113）
