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  -