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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.

  Solving Large Scale Learning Tasks - Essays dedicated to Katharina Morik on the occasion of her 60th birthday

Festschrift Solving Large Scale Learning Tasks

In celebration of Prof. Dr. Moriks 60th birthday, the Festschrift ''Solving Large Scale Learning Tasks'' covers research areas and researchers Katharina Morik worked with. This Festschrift has now been published at the Springer series on Lecture Notes in Artificial Intelligence.

Official presentation of the Festschrift will be on 20th of October at auditorium E23 at Otto-Hahn-Str. 14 starting 16.15 o’clock.

Articles in this Festschrift volume provide challenges and solutions from theoreticians and practitioners on data preprocessing, modeling, learning and evaluation. Topics include data mining and machine learning algorithms, feature selection and creation, optimization as well as efficiency of energy and communication. Talks for the presentation of the Festschrift are: Bart Goethals: k-Morik: Mining Patterns to Classify Cartified Images of Katharina, Arno Siebes: Sharing Data with Guaranteed Privacy, Nico Piatkowski: Compressible Reparametrization of Time-Variant Linear Dynamical Systems and Marco Stolpe: Distributed Support Vector Machines: An Overview.

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Ruhr Astroparticle and Plasma Physics Center (RAPP) inaugurated

Group picture participants RAPP workshop

In fall 2015, the Ruhr Astroparticle and Plasma Physics Center (RAPP center) was founded in order to combine research efforts within the fields of plasma- and particle-astrophysics in the Ruhr area. The three universities Ruhr-Universität Bochum, Technische Universität Dortmund and Universität Duisbug/Essen are located in a radius of 20 kilometers, enabling close collaboration between the universities.

The founding PIs include Prof. Wolfgang Rhode and Prof. Bernhard Spaan, who are also one of the project leaders of the SFB projects C3, respectively C5. During the Inauguration Workshop Katharina Morik gave an invited talk on the research impact of Data Mining for astroparticle physics.

In the RAPP center, about 80 researchers, from master’s level up to staff members, join forces to investigate fundamental physics questions and to break new ground by combining knowledge from the fields of plasma-, particle- and astrophysics.

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Annual meeting of DFG SPP 1736: Algorithms for BIG DATA in Dortmund

Participants of the SPP 1736-Workshops

From 26th to 28th of September the annual meeting of the DFG-SPP 1736: Algorithms for BIG DATA will be held in Dortmund. SPP members of the TU Dortmund are Johannes Fischer, Oliver Koch and Petra Mutzel. The SFB 876 participates via invited talks of Katharina Morik and Sangkyun Lee.

Focus of the SPP:

Computer systems pervade all parts of human activity and acquire, process, and exchange data at a rapidly increasing pace. As a consequence, we live in a Big Data world where information is accumulating at an exponential rate and often the real problem has shifted from collecting enough data to dealing with its impetuous growth and abundance. In fact, we often face poor scale-up behavior from algorithms that have been designed based on models of computation that are no longer realistic for big data.

While it is getting more and more difficult to build faster processors, the hardware industry keeps on increasing the number of processors/cores per board or graphics card, and also invests into improved storage technologies. However, all these investments are in vain, if we lack algorithmic methods that are able to efficiently utilize additional processors or memory features.

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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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