Towards Model-based Approximate Query Processing
| Type of publication: | Inproceedings |
| Citation: | mk:AIDB2019 |
| Booktitle: | Working Notes of the 1st International Workshop on Applied AI for Database Systems and Applications (held in conjunction with VLDB 2019) |
| Year: | 2019 |
| Month: | August |
| 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. |
| Keywords: | Approximate Query Processing, Databases, Deep Learning, SQL Queries, Sum-Product Networks |
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