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2020/02/20
Our paper “DeepDB: Learn from Data, not from Queries!” was accepted to VLDB2020 Tokyo
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. We propose to learn a pure data-driven model.
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2020/01/07
36th Chaos Communication Congress: How sustainable is Deep Learning?
Our lab members Nadja Geisler and Benjamin Hättasch talked about about the Deep Learning Hype and its consequences and impacts at the 36C3 in Leipzig
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2019/11/27
In the first submission cycle 3 out of 3 papers are accepted to SIGMOD 2020
IDEBench: A new Benchmark for Interactive Data Exploration / DB4ML: An In-Memory Database Kernel with Machine Learning Support / DBPal: A Fully Pluggable NL2SQL Training Pipeline
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2019/11/08
FG DB Symposium 2020 on ML for Systems and Systems for ML
Get your seat now: http://www.fgdb-symposium.de/ for more info!
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2019/10/15
Our paper “DBMS Fitting: Why should we learn what we already know?” was accepted to CIDR 2020
In this paper, we propose an alternative approach to learn DBMS components.
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