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Martin Pagel

Mo 12.10.2020, 16:00 – 16:45 Uhr, Online: "DeepDB - Learn from Data, not from Queries!" (Carsten Binnig, TU Darmstadt)

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, cardinality estimation, or even as an index. 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.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.

Carsten Binnig is a Full Professor in the Computer Science department at TU Darmstadt and an Adjunct Associate Professor in the Computer Science department at Brown University. Carsten received his PhD at the University of Heidelberg in 2008. Afterwards, he spent time as a postdoctoral researcher in the Systems Group at ETH Zurich and at SAP working on in-memory databases. Currently, his research focus is on the design of data management systems for modern hardware as well as modern workloads such as interactive data exploration and machine learning. His work has been awarded with a Google Faculty Award, as well as multiple best paper and best demo awards for his research.

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