%Aigaion2 BibTeX export from Knowledge Engineering Publications %Saturday 18 December 2021 12:02:48 AM @INPROCEEDINGS{mk:AIDB2019, author = {Kulessa, Moritz and Hilprecht, Benjamin and Molina, Alejandro and Kersting, Kristian and Binnig, Carsten}, keywords = {Approximate Query Processing, Databases, Deep Learning, SQL Queries, Sum-Product Networks}, month = aug, title = {Towards Model-based Approximate Query Processing}, booktitle = {Working Notes of the 1st International Workshop on Applied AI for Database Systems and Applications (held in conjunction with VLDB 2019)}, year = {2019}, location = {Los Angeles, USA}, url = {https://drive.google.com/file/d/1VAJGsf1vemhKl_IsLbznTv9TRwAcsaB3/view}, abstract = {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.} }