Our paper “Model-based Approximate Query Processing” was accepted to AIDB workshop at VLDB 2019

In this paper, we present a new approach to Approximate Query Processing (AQP) called Model-based AQP that leverages deep generative models learned over a dataset to answer SQL queries at interactive speeds. Different from classical AQP approaches, deep generative models allow us not only to compute approximate responses to ad-hoc queries even over rare sub-populations but additionally support a new class of queries called counterfactual queries enabling users to ask what-if queries.

Furthermore, we think that deep generative models can not only be used for AQP in databases but also have other applications for problems such as Query Optimization as well as Data Cleaning.