Berlin Big Data Center Phase II

© BBDC team

Funded by the German Federal Ministry of Education and Research (BMBF) and established in 2014, the Berlin Big Data Center (BBDC) is a national big data competence center led by the Technische Universität Berlin (TUB). In 2018, the BBDC entered into a subsequent three-year period due to an additional funding award by the BMBF. more to: Berlin Big Data Center Phase II

Berlin Center for Machine Learning

The Berlin Center for Machine Learning (BZML, Berliner Zentrum für Maschinelles Lernen) investigates methods of machine learning (ML), new complex models and mathematical foundations. more to: Berlin Center for Machine Learning

NebulaStream - Data Management for the Internet of Things

© NebulaStream project

NebulaStream is a joint research project between the DIMA group at TU Berlin and the DFKI IAM group. It develops the first general purpose, end-to-end data management system for the IoT. more to: NebulaStream - Data Management for the Internet of Things

FogGuru - Training the Next Generation of European Fog Computing Experts

© FogGuru project

FogGuru contributes to the rapidly emerging domain of fog computing with technologies for managing application resources, middlewares for easing the development of more to: FogGuru - Training the Next Generation of European Fog Computing Experts

Hawk - A Hardware Adaptive Query Compiler

© CoGaDB Team

The performance of modern processors is primarily bound by a fixed energy budget. This power wall forces processor vendors to specialize their processors to certain applications to provide the speedups users expect. more to: Hawk - A Hardware Adaptive Query Compiler

ADAM - Approximate Analysis of Massive Data Streams with Modern Hardware

© M. Kiefer

The Software Campus project ADAM addresses the connection between approximated analysis of data streams and the advantages of modern hardware architectures. more to: ADAM - Approximate Analysis of Massive Data Streams with Modern Hardware

LAPSE - A System Architecture for Communication-Efficient Distributed Machine Learning

© DIMA/Renz-Wieland

The Software Campus backed LAPSE aims to develop a system architecture that mitigates communication costs for distributed machine learning. more to: LAPSE - A System Architecture for Communication-Efficient Distributed Machine Learning

moreEVS

The moreEVS Project is part of a bilateral initiative for joint Sino-German research projects. The number of electric vehicles (EVs) in urban areas, and therefore also the need for charging stations is expected to increase rapidly in the following years. In this research project, DIMA will address the challenge of conducting large-scale data analysis efficiently and pair renewable energy power sources with EVs. more to: moreEVS

Rhino

The Rhino Software Campus project addresses scalable data stream processing and analytics challenges arising in Big Data, Cloud Computing, Industry 4.0, and IoT (Internet of Things) applications. Rhino also aims to develop a novel state management solution for scalable (i.e., low-latency, high-throughput) stream processing that enables fine-grained fault-tolerance, on-demand resource scaling, and load balancing in the presence of very large (e.g., hundreds of GBs) distributed state. In the end the objective is to develop a technological framework that seamlessly provides fault-tolerance, resource-scaling with zero downtime, and offers high-resource efficiency, lower operational costs, and reduced time-to-knowledge to end-users working on large-scale data applications. more to: Rhino

EDADS Software Campus Project

In the EDADS (Efficient Data Analysis Based on Data Summaries) Software Campus Project our principal aim is to design and implement sketch algorithms for streaming data in modern dataflow engines. Consequently, this would serve to reduce the size of data streams. Furthermore, in contrast to examining the entire dataset, the sketch could then be used by data analytics (e.g., for anomaly detection) and thereby shorten the data analysis execution time. more to: EDADS Software Campus Project

ExDRa

The Exploratory Data Science Over Raw Data (ExDRa) Project aims to conduct R&D in exploratory data science. The objective is to provide a support mechanism for exploratory data science and ease the analysis of distributed, heterogeneous raw data as well as to develop a research prototype suitable for real-world use cases. more to: ExDRa