At the Fraunhofer Development Center for X-ray Technology, the researchers are developing modern cognitive sensor systems that generate an extensive amount of data. From this data, information can be extracted in a targeted manner using intelligent software-based methods. These are then linked with further meta data in order to integrate the collected sensor data and their evaluation into an application-specific context. The knowledge generated and processed this way enables our customers to make well-founded, data-based decisions quickly.
When gathering information, the focus is on the collection of task-specific characteristics. We use classic algorithms or machine learning methods to acquire extracted features from the measured raw data. On this basis, an evaluation based on objective decision criteria can already be made.
The linking of collected information over several measurement cycles or a link with other complementary methods leads to aggregated knowledge. The semantic combination of the different data is an essential part of generating knowledge. In addition to the characteristics collected or the knowledge generated, this enables access to information from a comprehensive context.
This enables us to use visualizations or methods for data analytics to prepare recommendations for actions, for working out such actions or for deriving automated decisions from them.