TY - GEN ID - bh:deepDB2019 T1 - DeepDB: Learn from Data, not from Queries! A1 - Hilprecht, Benjamin A1 - Schmidt, Andreas A1 - Kulessa, Moritz A1 - Molina, Alejandro A1 - Kersting, Kristian A1 - Binnig, Carsten Y1 - 2019 UR - https://arxiv.org/pdf/1909.00607.pdf KW - Databases KW - Deep Learning KW - SQL Queries KW - Sum-Product Networks N2 - The typical approach for learned DBMS components is to capture the behavior by running a representative set of queries and use the observations to train a machine learning model. This workload-driven approach, however, has two major downsides. First, collecting the training data can be very expensive, since all queries need to be executed on potentially large databases. Second, training data has to be recollected when the workload and the data changes. To overcome these limitations, we take a different route: we propose to learn a pure data-driven model that can be used for different tasks such as query answering or cardinality estimation. This data-driven model also supports ad-hoc queries and updates of the data without the need of full retraining when the workload or data changes. Indeed, one may now expect that this comes at a price of lower accuracy since workload-driven models can make use of more information. However, this is not the case. The results of our empirical evaluation demonstrate that our data-driven approach not only provides better accuracy than state-of-the-art learned components but also generalizes better to unseen queries. ER -