Learning comprehensible models for analysis and predictions in scientific databases

Efficient handling and analysis of experimental measurements is an essential part of research and development in a multitude of disciplines (e. g., engineering, chemistry, biology), since these contain information about the underlying processes. Researchers investigate processes by running experiments and gathering potentially a huge amount of data which is then to be evaluated. For environmental monitoring wireless sensor networks are used to collect data at spatially and temporally discrete positions. In mechanical engineering and related areas, potentially complex test-benches are set up and observations are recorded. Besides an efficient and effective way of exploring multiple results, researchers strive to discover correlations within the measured data. Moreover, model-based prediction of expected measurements can be highly beneficial for designing further experiments.