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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.

B2-Project PAMONO publication selected as leading article for current Sensors journal issue

Sensors Journal Cover

The most recent B2-Project publication "Application of the PAMONO-sensor for Quantification of Microvesicles and Determination of Nano-particle Size Distribution" has been selected by the journal Sensors as the leading article for their current issue. The article is available via Open Access on the journals web site. The article was co-authored by Alexander Schramm, project leader of SFB-project C1.


The PAMONO-sensor (plasmon assisted microscopy of nano-objects) demonstrated an ability to detect and quantify individual viruses and virus-like particles. However, another group of biological vesicles—microvesicles (100–1000 nm)—also attracts growing interest as biomarkers of different pathologies and needs development of novel techniques for characterization. This work shows the applicability of a PAMONO-sensor for selective detection of microvesicles in aquatic samples. The sensor permits comparison of relative concentrations of microvesicles between samples. We also study a possibility of repeated use of a sensor chip after elution of the microvesicle capturing layer. Moreover, we improve the detection features of the PAMONO-sensor. The detection process utilizes novel machine learning techniques on the sensor image data to estimate particle size distributions of nano-particles in polydisperse samples. Altogether, our findings expand analytical features and the application field of the PAMONO-sensor. They can also serve for a maturation of diagnostic tools based on the PAMONO-sensor platform.

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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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