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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis


The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.



Fritz-Lampert-Award of the TRANSAID-foundation for cancer-suffering children for Alexander Schramm

Alexander Schramm

The Fritz-Lampert-Award of the TRANSAID-foundation for cancer-suffering children of the year 2016 has been awarded to Alexander Schramm (C1), head of the pediatric-oncologic research lab at the University Clinic Essen. The german-russian research award recognises excellent researchers and their work in the field of pediatric hematology and oncology for fundamental and clinical research. The award has been handed over at the semi-annual meeting of the Gesellschaft für Pädiatrische Onkologie und Hämatologie (GPOH) in Frankfurt at the 8th of November.
Recognised was his work in the publication Mutational dynamics between primary and relapse neuroblastomas, published together with national and international researchers in the Nature Genetics Journal. Beside Prof. Dr. Schramm the two further C1 project leaders, Prof. Dr. Sven Rahmann and Dr. Sangkyun Lee, also contributed to the publication.
Major concern of doctors is the recurrence of tumors, often leading to worse treatment results. Novel data analysis techniques can focus on differences between primary (at diagnosis) and recurrent neuroblastoma cancer cell genetic profiles. Found genetic patterns provide a chance for upcoming, target-specific therapies.

DockHa - Personal Hadoop cluster on Docker Swarm in minutes

Analysing Big Data typically involves developing for or comparing to Hadoop. For researching new algorithms, a personal Hadoop cluster, running independently of other software or other Hadoop clusters, should provide a sealed environment for testing and benchmarking. Easy setup, resizing and stopping enables rapid prototyping on a containerized playground.

DockHa is a project developed at the Artificial Intelligence Group, TU Dortmund University, that aims to simplify and automate the setup of independent Hadoop clusters in the SFB 876 Docker Swarm cluster. The Hadoop properties and setup parameters can be modified to suit the application. More information can be found in the software section (DockHa) and the Bitbucket repository (DockHa-Repository).

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