Data Management
The Data Management Lab was founded in 2017 by Prof. Carsten Binnig and is part of the Department of Computer Science at TU Darmstadt in Darmstadt, Germany.
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.
Our lab page lists all team members. Please see our job listings if you are interested in working with us.
Research
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.
Teaching
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.
News
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2020/08/11
Our paper “It’s AI Match: A Two-Step Approach for Schema Matching Using Embeddings” was accepted to workshop AIDB @ VLDB 2020
In this paper, we analyze how neural embeddings can be used in data integration.
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2020/05/19
Our paper “Sharing Opportunities for OLTP Workloads in Different Isolation Levels” was accepted for the PVLDB Vol 13, 2020
In this paper, we present an approach for merging read statements within interactively submitted multi-statement transactions consisting of reads and writes.
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2020/05/07
Our paper “Towards Robust and Transparent Natural Language Interfaces for Databases“ was accepted for the ”Workshop on Human-In-the-Loop Data Analytics“ @HILDA/SIGMOD 2020
In recent years the field of research on natural language interfaces for databases (NLIDBs) has progressed considerably…
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2020/04/13
Our paper “The Tale of 1000 Cores: An Evaluation of Concurrency Control on Real(ly) Large Multi-Socket Hardware“ was accepted for the workshop ”Data Management on New Hardware" @SIGMOD 2020
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.
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2020/04/01
In both submission cycles 7 out of 7 papers are accepted to SIGMOD 2020
The accepted papers in both cycles are…