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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.
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  IDS 2020 – Third Edition of the Industrial Data Science Conference Goes Online

The third Industrial Data Science Conference (IDS 2020), taking place on 21th & 22th October 2020, brings together experts from various industries with data science application examples and best practices in order to promote the exchange of experience and discussions among colleagues and experts.

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Digitisation, the Internet of Things (IoT) and industry 4.0 technologies are changing entire industries, enabling the capture of vast amounts of data of all kinds, including big data and streaming data, structured and unstructured data, text, images, audio and sensor data.

Data Science, Data Mining, Process Mining, machine learning and Predective Analytics offer the opportunity to generate enormous competitive advantages from data. Hence IDS 2020 will focus on these aspects.

The key topics of the event are:

  • Industrial applications of Data Science
  • Success factors for data science projects
  • Current research activities
  • Strategic integration of data science in the company

Further information and registration can be found at the following address: IDS 2020

If you have any questions, don’t hesitate to contact us at ids2020@industrial-data-science.de

 

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  Best Paper Award For scientists from subproject B4 at renowned IEEE conference on communication systems

A conference paper developed within the DFG Collaborative Research Center 876 ("Data Analysis under Resource Constraints") by communication experts (Benjamin Sliwa, Christian Wietfeld from the Chair of Communication Networks of the Faculty ETIT) with experts in machine learning (Nico Piatkowski, former SFB 876 now ML2R) was awarded a Best Paper Award at the IEEE Flagship Conference "International Communications Conference (ICC)".

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At the ICC 2020, which was originally planned for this year in Dublin, over 2100 papers were presented in a virtualized format. The SFB 876 paper awarded at the conference, entitled "LIMITS: Lightweight Machine Learning for IoT Systems with Resource Limitations", presents the novel open source framework LIghtweight Machine Learning for IoT Systems (LIMITS), which uses a platform-in-the-loop approach that explicitly takes into account the concrete software generation tools (the so-called compilation toolchain) of the Internet-of-Things (IoT) target platform.

LIMITS focuses on comprehensive tasks such as the automation of experiments and data acquisition, platform-specific code generation and the so-called sweet spot determination for optimal parameter combinations. In two case studies focusing on cellular data rate prediction and radio-based vehicle classification, LIMITS will be validated by comparing different learning models and real IoT platforms with memory constraints from 16 kB to 4 MB. Furthermore, its potential as a catalyst for the development of IoT systems with machine learning will be demonstrated.

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