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Fast CSV loading using gpus – TUB and DFKI paper accepted at BTW 2021
A paper on the accelerated loading of CSV data using GPUs and RDMA by researchers from the Database Systems and Information Management Group (DIMA) at TU Berlin and the Intelligent Analytics for Massive Data (IAM) research group at DFKI was accepted at the 19th symposium “Database Systems for Business, Technology and Web” (BTW 2021), which will take place from September 20 – 24, 2021.
TUB internet network research papers accepted at CoNEXT 2020 and COVID-19 network impacts workshop 2020
Papers by researchers in the Internet Network Architectures (INET) group at TU Berlin, headed by Prof. Dr. Georgios Smaragdakis, were accepted or presentation or publication at CoNEXT 2020, COVID-19 Network Impacts Workshop 2020 and IEEE Transactions on Network and Service Management, 2020.
BIFOLD researchers are among the most cited worldwide
BIFOLD Co-Director Prof. Dr. Klaus-Robert Müller and Principal Investigators Prof. Dr. Giuseppe Caire and Prof. Dr. Frank Noé are featured in the 2020 Highly Cited Researchers™ list, either Cross-Field or in the Computer Sciences.
Major extension of the EDBT 2019 best paper by TU Berlin and DFKI researchers accepted for publication in TODS
The paper “Scotty: General and Efficient Open-Source Window Aggregation for Stream Processing Systems” by J. Traub et al. was accepted for publication at ACM Transactions on Database Systems (TODS). This extended journal paper is a major extension of the EDBT best paper titled Efficient “Window Aggregation with General Stream Slicing” from 2019 by the same authors from the DIMA group and the Intelligent Analytics for Massive Data (IAM) group at DFKI. Among other extensions, the new journal paper was extended with detailed algorithm specifications, API-examples, and examples for using Scotty in different streaming systems.
Multiple internet network architectures papers presented at IMC ’20
BIFOLD’s Principal Investigators Prof. Dr. Smaragdakis, Prof. Dr. Anja Feldmann and other researchers from the Internet Network Architectures (INET) group at TU Berlin presented four papers at the 20th ACM Internet Measurements Conference (IMC ‘20), which took place from October 27 – 29, 2020 as a virtual event. Among other topics, they examined the effects of the first pandemic lockdown on Internet traffic.
“Data is the new soil!” – Interview with Prof. Markl
In an interview with the German newspaper ‘Der Tagesspiegel’, one of BIFOLD’s directors, Prof. Dr. Markl, explains necessary steps to drive Europe forward in terms of data sovereignity and innovation ecosystems.
BIFOLD researchers at DHZB developed AI to predict kidney failure
BIFOLD Associated Investigator PD Dr. Meyer (DHZB) and Principal Investigator Prof. Dr. Kühne (DHZB, Charité) developed a recurrent neural network (RNN) which is able to predict severe kidney failure better than human professionals. The corresponding paper was published in “Nature Partner Journal (npj) Digital Medicine.”
Paper on novel sketch maintenance system accepted for publication in PVLDB Vol. 14
The Paper “Scotch: Generating FPGA-Accelerators for Sketching at Line Rate” by Martin Kiefer, Ilias Poulakis, Sebastian Breß and Volker Markl will be featured in Proceedings of the VLDB Endowment (PVLDB), Volume 14. In their paper, the authors propose Scotch, a novel system for accelerating sketch maintenance using the custom FPGA hardware.
BIFOLD database systems research papers were accepted at CIDR 2021
Researchers at the Database Systems and Information Management (DIMA) group at TU Berlin and the Intelligent Analytics for Massive Data (IAM) group at DFKI have been informed that their papers were accepted for presentation at the 11th Annual Conference on Innovative Data Systems Research (CIDR ’21) which will be held as a virtual event on January 11-15, 2021.
BIFOLD research paper on machine learning for quantum chemistry published in Nature Communications
The Paper “Quantum chemical accuracy from density functional approximations via machine learning” by Mihail Bogojeski, Leslie Vogt-Maranto, Mark E. Tuckerman, Klaus-Robert Müller, Kieron Burke was published in Nature Communications. In this paper, the authors leverage machine learning to calculate coupled-cluster energies from DFT densities, reaching much better quantum chemical accuracy on test data than achieved with previous available methods. Moreover, their approach significantly reduced the amount of training data required.