Research on Robust and Adaptive Learned Approximate Query-Processing Method
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.
国家科技期刊平台
NSTL
万方数据
维普
