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.
The Deutsche Forschungsgemeinschaft (DFG) granted the next four years of the collaborative research center SFB 876. The following projects will continue their research:
- A1 Data Mining for Ubiquitous System Software
- A2 Algorithmic aspects of learning methods in embedded systems
- A3 Methods for Efficient Resource Utilization in Machine Learning Algorithms
- A4 Resource efficient and distributed platforms for integrative data analysis
- A6 Resource-efficient Graph Mining
- B2 Resource optimizing real time analysis of artifactious image sequences for the detection of nano objects
- B3 Data Mining on Sensor Data of Automated Processes
- B4 Analysis and Communication for Dynamic Traffic Prognosis
- C1 Feature selection in high dimensional data for risk prognosis in oncology
- C3 Multi-level statistical analysis of high-frequency spatio-temporal process data
- C4 Regression approaches for large-scale high-dimensional data
- C5 Real-Time Analysis and Storage of High-Volume Data in Particle Physics
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