Roman Heinrich and Manisha Luthra will present their work at the conference to be held from 27th June – 30th June in Copenhagen Denmark.

We congratulate them on these acceptances!

Members of our group present their work at the conference and the workshops -- catch up on site or online

We are glad to be in Philly -- and brought talks, demos and posters over several topics with us:

Quest: A Query-Driven Framework to Explain Classification Models on Tabular Data

Nadja Geisler will present “Quest: A Query-Driven Framework to Explain Classification Models on Tabular Data” at the HILDA 2022 workshops co-located with the International Conference on Management of Data 2022 in Philadelphia.

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.

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.

She got the award for her presentation on “Quest: A Query-driven Explanation Framework for Black-Box Classifiers on Tabular Data”

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”.

In this talk we present our vision to tackle the high costs and inflexibility of workload-driven learning.

We propose to extend the classical ACID properties with a new Verifiability component.