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Dr. rer. nat. habil. Stefan Bosse: Researcher & Lecturer, Bremen, Germany
Visiting Professor (W3 Representation) at University of Koblenz-Landau, Faculty Computer Science, Koblenz
Privatdozent (PD) at University of Bremen, Dept. of Mathematics & Computer Science
Topics: Science and Education
Domains = Computer Science ∩ Systems Engineering ∩ Materials Science ∩ Physics ∩ Social Science
Supply Chain: Model → Hardware → Software → Complete
In association with BSSLAB: Industrial Research and Development Laboratory
Today sensor data processing and information mining become more and more complex concerning the amount of sensor data to be processed, the data dimension, the data quality, and the relationship between derived information and input data. This is the case especially in large-scale sensing and measuring processes embedded in Cloud environments. Measuring uncertainties, calibration errors, and unreliability of sensors have a significant impact on the derivation quality of suitable information. In the technical and industrial context the raising complexity and distribution of data processing is a special issue. Commonly, information is derived from raw input data by using some kind of mathematical model and functions, but often being incomplete or unknown. If reasoning of statements is primarily desired, Machine Learning can be an alternative. Traditionally, sensor data is acquired and delivered to and processed by a central processing unit. The deployment of distributed Machine Learning using mobile Agents forming self-organizing and self-adaptive systems (self-X) pose the benefit for the enhancement of the sensor and data processing in technical and industrial systems. This also addresses the quality of the computed statements, e.g., an accurate prediction of run-time parameters like mechanical loads or health conditions, the efficiency, and the reliability in the presence of partial system failures.
From science to education - a straightforward path. Among basic lectures teaching the core concepts there is a relevant demand for teaching state-of-the-art science by transferring, condensing, and presenting scientific research in advanced lectures. The raising distributed computing in heterogeneous environments (mobile, Internet, Clouds, embedded) demands for the understanding and practical deployment of modern core concepts addressing adaptive and self-organizing distributed computing, e.g. by using mobile agents. Among core concepts the relation of fundamental methodologies with technology is a key factor in the design of future large-scale computing systems.