SFB 876:  Providing Information by Resource-Constrained Data Analysis

• Subject Area: Computer Science, Systems and Electrical Engineering ; Biology ; Mechanical and Industrial Engineering ; Mathematics ; Medicine ; Physics
• Term: from 2011 to 2022
• Website: Homepage
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 124020371

The research center SFB 876 brings together the research fields of data analysis (Big Data Analytics, Data Mining, Machine Learning, Statistics) and Cyber-Physical Systems (Embedded Systems) and enhances their methods such that information from distributed, dynamic masses of data becomes available anytime and anywhere.Resource constraints arise from the need to handle large-volume, large-dimensional, or high-velocity data in an efficient way. The relation between the data size and the computing resources determines the necessary scalability of algorithms. For small, ubiquitous devices, the resource constraints are directly apparent, but they hold as well for large computing facilities in the case of extremely large, high-dimensional, dynamic data. Whereas runtime is a standard consideration, attention of the research community for further resources such as memory, energy, and communication has come to focus in recent research. Data analysis algorithms are investigated for several programming paradigms (data streams, MapReduce) and execution platforms (FPGA, GPU), taking their resource requirements into account. Resource models are established for the correct evaluation of resources, namely energy consumption or communication. In these perspectives, SFB 876 is a pioneer.Projects in SFB 876 are coordinated as follows. The challenges in the B projects stem from local, mobile, ubiquitous systems such as distributed embedded systems or small virus scanners. Resource constraints in the C projects arise from the high dimensionality, dynamics, and volume of the data, as are generated, for instance, by astro and particle physics. The overarching projects in the A domain encompass both other pillars.

• DFG Programme: Collaborative Research Centres

Completed projects

• A01 - Data Mining for Ubiquitous System Software (Project Heads Chen, Jian-Jia ;  Morik, Katharina ;  Spinczyk, Olaf )
• A02 - Algorithmic aspects of learning methods in embedded systems (Project Heads Schubert, Erich ;  Sohler, Christian ;  Teubner, Jens ;  Vahrenhold, Jan )
• A03 - Methods for Efficient Resource Utilization in Machine Learning Algorithms (Project Heads Chen, Jian-Jia ;  Marwedel, Peter ;  Rahnenführer, Jörg )
• A04 - Resource efficient and distributed platforms for integrative data analysis (Project Heads ten Hompel, Michael ;  Marwedel, Peter ;  Spinczyk, Olaf ;  Wietfeld, Christian )
• A05 - Exchange and Fusion of Information under Availability and Confidentiality Requirements in Multi-Agent Systems (Project Heads Biskup, Joachim ;  Kern-Isberner, Gabriele )
• A06 - Resource-efficient Graph Mining (Project Heads Fischer, Johannes Christian ;  Kersting, Kristian ;  Kriege, Nils ;  Mutzel, Petra ;  Sohler, Christian ;  Weichert, Frank )
• B01 - Analysis of Spectrometry Data with Restricted Resources (Project Heads Rahmann, Sven ;  Rahnenführer, Jörg )
• B02 - Resource optimizing real time analysis of artifactious image sequences for the detection of nano objects (Project Heads Chen, Jian-Jia ;  Hergenröder, Roland ;  Marwedel, Peter ;  Müller, Heinrich ;  Weichert, Frank ;  Zybin, Alexander )
• B03 - Data Mining on Sensor Data of Automated Processes (Project Heads Deuse, Jochen ;  Morik, Katharina ;  Wiederkehr, Petra )
• B04 - Analysis and Communication for dynamic traffic prognosis (Project Heads Kersting, Kristian ;  Liebig, Thomas ;  Schreckenberg, Michael ;  Wietfeld, Christian )
• C01 - Feature selection in high dimensional data for risk prognosis in oncology (Project Heads Köster, Johannes ;  Lee, Sangkyun ;  Morik, Katharina ;  Rahmann, Sven ;  Schramm, Alexander )
• C03 - Multi-level statistical analysis of high-frequency spatio-temporal process data (Project Heads Fried, Roland ;  Morik, Katharina ;  Rhode, Wolfgang ;  Ruhe, Tim )
• C04 - Regression approaches for large-scale highdimensional data (Project Heads Ickstadt, Katja ;  Munteanu, Alexander ;  Sohler, Christian )
• C05 - Real-Time Analysis and Storage of High-Volume Data in Particle Physics (Project Heads Albrecht, Johannes ;  Spaan, Bernhard ;  Teubner, Jens )
• MGK - Integrated Research Training Group (Project Head Rhode, Wolfgang )
• Z - Central tasks of the collaborative research center (Project Head Morik, Katharina )

• Applicant Institution: Technische Universität Dortmund
• Business and Industry: ARTES Biotechnology GmbH
• Participating Institution: Leibniz-Institut für Analytische Wissenschaften -ISAS- e.V.; Paul-Ehrlich-Institut; Bundesinstitut für Impfstoffe und biomedizinische Arzneimittel
• Participating University: Universität Duisburg-Essen
• Spokesperson: Professorin Dr. Katharina Morik