Data Analysis Systems
DAS conducts applied research in the following fields:
- Data management and analysis: e.g., cost efficiency for storage, transfer and searches with respect to time and storage space;
- Process analysis: e.g., identification of statistical correlations;
- Process optimization and control: e.g., virtual sensors for the chemical industry, energy efficient controls based on model-predictive approaches.
For this project definition, knowledge and model fragments of various types and different semantic levels often play a role, e.g., statistical correlations, prognosis models, rules and ontologies. The goal is to apply suitable methods from mathematics and computer science to make the available sources of information useful for a respective application. In addition to high performance computing aspects for parallelized and distributed data analysis and data mining, in particular the following technologies and approaches play a central role:
- Machine learning and knowledge extraction via semantic inference: e.g., interpretable models based on semantic technologies; semi-supervised and kernel-based methods and innovative similarity metrics; virtual sensors and model-based control approaches for optimum control of machines or improved prediction models
- Semantic data management: e.g., semantics, meta-modeling, data warehousing, stream data warehousing, knowledge representation models to improve data quality
- Stream data analysis: e.g., collection, handling and analysis of large quantities of data based on data warehousing, complex event processing, stream data mining and incremental learning.
Keywords
Data management, knowledge representation, knowledge extraction from data, data-driven modeling for optimization and prognosis, model predictive control.