The Berlin Big Data Centre (BBDC) develops highly innovative technologies to organize the vast amounts of data and to derive informed decisions from these in order to create economic and social value. This is achieved through the merger of the previously isolated disciplines of data management and machine learning. The technologies of the center reduce the cost of analysis of Big Data, increase the group of people who can perform large scale data analysis and expand the leading position of Germany in this field in science and industry. The focus is on three exemplary economically, scientifically and socially relevant application areas: materials science, medicine, and information marketplaces. Based on worldwide recognized leading-edge research we want to enable automatic optimization, parallelization, and scalable and adaptive processing algorithms. This covers work in the areas of machine learning, linear algebra, statistics, probability theory, computer linguistics and signal processing.

Publications

2018
From Application to Disk: Tracing I/O Through the Big Data Stack High Performance Computing ISC High Performance 2018 International Workshops, Frankfurt/Main, Germany, June 24 - 28, 2018, Revised Selected Papers, Workshop on Performance and Scalability of Storage Systems (WOPSSS), pp. 89-102, 2018 Robert Schmidtke, Florian Schintke, Thorsten Schütt BibTeX
DOI
BBDC
Improving I/O Performance Through Colocating Interrelated Input Data and Near-Optimal Load Balancing Proceedings of the IPDPSW; Fourth IEEE International Workshop on High-Performance Big Data, Deep Learning, and Cloud Computing (HPBDC), Vol.2018, pp. 448-457, 2018 Felix Seibert, Mathias Peters, Florian Schintke BibTeX
DOI
BBDC
2016
Big Data Analytics on Cray XC Series DataWarp using Hadoop, Spark and Flink CUG Proceedings, 2016 Robert Schmidtke, Guido Laubender, Thomas Steinke PDF
BibTeX
BBDC