Exploiting Machine Learning Models for Approximate Query Processing

Abstract

Approximate query processing can help reduce response time for aggregate queries in exploratory data analysis. In this study, we describe basic query transformation rules for processing approximate queries using synthetic data tables or inferential models. Based on the preliminary experimental results, we confirm that ML models can be used to provide approximate query results in response times acceptable for applications.