TY - CONF ID - mk:AIDB2019 T1 - Towards Model-based Approximate Query Processing A1 - Kulessa, Moritz A1 - Hilprecht, Benjamin A1 - Molina, Alejandro A1 - Kersting, Kristian A1 - Binnig, Carsten TI - Working Notes of the 1st International Workshop on Applied AI for Database Systems and Applications (held in conjunction with VLDB 2019) Y1 - 2019 CY - Los Angeles, USA UR - https://drive.google.com/file/d/1VAJGsf1vemhKl_IsLbznTv9TRwAcsaB3/view KW - Approximate Query Processing KW - Databases KW - Deep Learning KW - SQL Queries KW - Sum-Product Networks N2 - 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. ER -