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.
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.
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…
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  Cores” and analyse in-memory DBMSs.
The accepted papers in both cycles are…
Our Paper “Summarization Beyond News: The Automatically Acquired Fandom Corpora” was accepted to LREC 2020
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
In this paper, we propose an alternative approach to learn DBMS components.