Collaborative Research Center 876 "Availability of Information through Analysis under Resource Restrictions"
| SFB 876 | |
|---|---|
| Spokeswoman | Katharina Morik |
| Main location | TU Dortmund University |
| Sponsor | DFG |
| Funding period | 2011-2022 |
| Website | SFB 876 |
The Collaborative Research Center (SFB) 876 is a research project at the TU Dortmund funded by the German Research Foundation . The SFB 876 was set up in 2011 on the initiative of Katharina Morik , Chair of Artificial Intelligence. For the first time, the topics of machine learning and computer architecture were brought together. The overriding topic of the SFB is data analysis under resource restrictions. The resource limitation is defined as the ratio of technical execution capacity on the one hand and dimensionality and mass of the data on the other. This results in the three project areas: data analysis, embedded systems and high data volumes. The SFB currently comprises 13 sub-projects and a graduate college in which employees from 17 chairs from the faculties of computer science, electrical engineering and information technology, mechanical engineering, physics, statistics and biomedicine conduct research. In addition, there was and is cooperation with non-university institutions.
Project areas
The Collaborative Research Center 876 is divided into three sub-areas to which the respective research projects are assigned.
A: data analysis
Project area A provides the theoretical basis for bringing the project areas together. It creates the basis for the projects in the other areas and their specific tasks.
Projects in project area A:
- A1 - Data mining for ubiquitous system software
- A2 - Algorithms of learning processes in embedded systems
- A3 - Methods for the efficient use of resources in machine learning algorithms
- A4 - Resource-efficient and distributed platforms for integrative data analysis
- A5 - Exchange and fusion of information subject to availability and confidentiality requirements in multi-agent systems (ended after phase 1)
- A6 - Resource-efficient analysis of graphs
B: Embedded Systems
Project area B deals with resource restrictions that occur on local and small mobile devices. The project area examines the sampling, aggregation and analysis of data from distributed sources or learning on (distributed) small devices.
Projects in project area B:
- B1 - Resource-limited analysis of spectrometry data (ended after phase 2)
- B2 - Resource-optimized real-time analysis of strongly artifact-laden image sequences for the detection of nano-objects (continuation as a transfer project with the Paul Ehrlich Institute of ARTES Biotechnology GmbH in phase 3)
- B3 - data mining in sensor data of automated processes
- B4 - Analysis and communication for dynamic traffic forecast
C: High volume of data
Project area C deals with resource constraints that arise from dynamic and high-dimensional data.
Projects in project area C:
- C1 - Feature selection in high-dimensional data using the example of risk prognosis in oncology
- C3 - Multi-level statistical analysis of high-frequency spatiotemporal process data
- C4 - Regression method for very large, high-dimensional data
- C5 - Real-time analysis and storage for high-volume data from particle physics
Web links
Individual evidence
- ↑ DFG - SFB 876: Availability of information through analysis under resource restrictions. Retrieved September 24, 2018 .