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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.

  Professor Michael ten Hompel Awarded with Honorary Doctorate

Michael ten Hompel

On June 30th, 2017, professor Michael ten Hompel was awarded an honorary doctorate for his special scientific merits of logistics research. Ten Hompel is Director of the Chair of Materials Handling and Warehousing at TU Dortmund University as well as Manager of the Fraunhofer Institute for Material Flow and Logistics. The doctorate was awarded by the Hungarian University of Miskolc.


KDD Journalism Workshop

This is an exciting time for journalism. The rise of online media and computational journalism proposes new opportunities for investigation, presentation and distribution of news. One opportunity is collaborative data science-facilitated journalism: for the Panama Papers project, 2.6 Terabyte of offshore documents were analysed; the resulting stories caused global attention and investigations in 79 countries, and led to the resignation of several heads of state. Other opportunities arise from new formats such as Virtual Reality, Drone and Robot Journalism which offer completely new forms of storytelling. In addition to new opportunities, there are challenges that are proving critical in 2017. The role of social media on the distribution of information is controversial. Fake news and filter bubbles are blamed for political, social and economic unrest, and they have caused a crisis in trust in the news industry. In turn, scientists use methods from Machine Learning and Large Scale Graph Analysis to study their effects on society.

These topics will be discussed at the first workshop on DATA SCIENCE + JOURNALISM at KDD 2017 in Halifax, Canada. The workshop is organized collaboratively by scientists from SFB 876, Project A6, the university of Illinois and Bloomberg.

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Industrial Data Science Conference

The Industrial Data Science Conference gathers experts from various industries and focuses on data science applications in industry, use cases, and best practices to foster the exchange of experience, discussions with peers and experts, and learning from presenters and other attendees.

Digitization, the Internet of Things (IoT), the industrial internet, and Industry 4.0 technologies are transforming complete industries and allow the collection of enormous amounts of data of various types, including Big Data and Streaming Data, structured and unstructured data, text, image, audio, and sensor data. Data Science, Data Mining, Process Mining, Machine Learning, and Predictive Analytics offer the opportunity to generate enormous value and a competitive advantage. Typical use cases include demand forecasting, price forecasting, predictive maintenance, machine failure prediction and prevention, critical event prediction and prevention, product quality prediction, process optimization, mixture of ingredients optimization, and assembly plan predictions for new product designs in industries like automotive, aviation, energy, manufacturing, metal, etc.

Join your peers in the analytics community at IDS 2017 as we explore breakthrough research and innovative case studies that discuss how to best create value from your data using advanced analytics.

Date September 5th, 2017
Location TU Dortmund University
Web RapidMiner IDS 2017

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Resource-aware Machine Learning - International Summer School 2017


Big data in machine learning is the future. But how to deal with data analysis and limited resources: Computational power, data distribution, energy or memory? From September 25th to 28th, TU Dortmund University, Germany, hosts the 4th summer school on resource-aware machine learning. Further information and online registration at: http://sfb876.tu-dortmund.de/SummerSchool2017

Topics of the lectures include: Machine learning on FPGAs, Deep Learning, Probabilistic Graphical Models and Ultra Low Power Learning.

Exercises help bringing the contents of the lectures to life. The PhyNode low power computation platform was developed at the collaborative research center SFB 876. It enables sensing and machine learning for transport and logistic scenarios. These devices provide the background for hands-on experiments with the nodes in the freshly built logistics test lab. Solve prediction tasks under very constrained resources and balance accuracy versus energy.

The summer school is open to advanced graduate, post-graduate students as well as industry professionals from across the globe, who are eager to learn about cutting edge techniques for machine learning with constrained resources.

Excellent students may apply for a student grant supporting travel and accommodation. Deadline for application is July 15th.

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