%Aigaion2 BibTeX export from Knowledge Engineering Publications
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@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.}
}