The ubiquitous flood of generated data places enormous demands on efficient data access, data management and data archiving and is one of the main drivers for the rapid development and advancement of modern data platforms. The availability of large, heterogeneous and rapidly produced data and applications are therefore the main motivations for the development of novel methods and system architectures for the management of large databases. As a direct consequence, the basic processing scheme changes from "process-oriented" to "data-oriented". Based on real application cases and requirements from the administration of scientific data (e.g. from earth observation and radio astronomy), the working group develops novel methods and system concepts which lead to practicable system developments.
In this context, the main research areas of the working group include the development of new methods and processing strategies for large, heterogeneous, multidimensional data sets in distributed IT infrastructures, such as public/private computer infrastructures. In particular, the size and heterogeneity of the data present great challenges for efficient access methods. Multidimensional indexing methods and semantic annotations based on knowledge models are the core components for the efficient identification and location of relevant data. Data access can be further improved by exploiting domain knowledge in data organization and by considering competing access patterns. On this basis, flexible analysis platforms can be built that allow the optimization of data processing, access and archiving.
The research is carried out in cooperation with the German Remote Sensing Data Center, the Max Planck Institute for Radio Astronomy, the Thuringian State Observatory Tautenburg, as well as with partners of the Technical University Ilmenau and the Friedrich Schiller University Jena.