In this paper, we set out the goal to revisit the results of “Starring into the Abyss […] of Concurrency Control with [1000] Cores” and analyse in-memory DBMSs.

The accepted papers in both cycles are…

We propose a way to automatically construct non-news summarization corpora and create three corpora using that approach

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.

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

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

Get your seat now: http://www.fgdb-symposium.de/ for more info!

In this paper, we propose an alternative approach to learn DBMS components.

As a first contribution of this paper, we propose a new switch architecture that can be employed as an in-network co-processor for analytical SQL workloads.

Abdallah Salama received the Best Student Paper Award for his paper “XAI – A Middleware for Scalable AI” which runs on top of existing deep learning frameworks such as TensorFlow or MXNet and automates the hyper-parameter search for distributed deep