TU Darmstadt is one of the leading technical universities in Germany. Its Computer Science department consistently ranks among the top 5 in Germany. TU Darmstadt offers a unique environment for research in data management due to strong cooperations with other groups like the Machine Learning Lab or Distributed Systems Programming Lab, as well as ample interdisciplinary interaction.
We conduct research in several areas of databases and data management in conjunction with artificial intelligence (AI). The groups main focus areas are Systems for AI and AI for Systems.
Our lab regularly offers courses, seminars and labs on various data management topics. Furthermore, we offer term projects and Bachelors and Masters thesis projects with different focus areas.
Our demo paper "Demonstrating ASET: Ad-hoc Structured Exploration of Text Collections" by Benjamin Hättasch and Jan-Micha Bodensohn will be presented at the International Conference on Management of Data 2022 in Philadelphia
In this demo, we present ASET, a novel tool to explore the contents of unstructured data (text) by automatically transforming relevant parts into tabular form. go
Two papers on OLTP accepted in the first round of submissions for ACM SIGMOD/PODS 2022 International Conference on Management of Data
Nils Boeschen and Matthias Jasny will present their work at the International Conference on Management of Data in 2022
Both papers propose new systems to accelerate OLTP workloads: “GaccO – A GPU-accelerated OLTP DBMS” uses GPUs to execute transactions of single-node DBMSs, while “P4DB – The Case for In-Network OLTP” offloads transaction execution of distributed DBMSs into programmable switches. go
She got the award for her presentation on "Quest: A Query-driven Explanation Framework for Black-Box Classifiers on Tabular Data"
Three papers on Shared Tasks for NLIDBs, Explainable AI, and Interactive Text Exploration accepted to DESIRES 2021
Nadja Geisler and Benjamin Hättasch will present their work at the second conference on Design of Experimental Search & Information REtrieval Systems in Padova
The three papers are “Netted?! How to Improve the Usefulness of Spider & Co.”, “Quest: A Query-driven Explanation Framework for Black-Box Classifiers on Tabular Data”, and “WannaDB: Ad-hoc Structured Exploration of Text Collections Using Queries”. go