Machine learning for smart systems
Machine learning algorithms fuel future smart systems, make them more adaptable to new situations and more intuitive to use. Whether in factories or smartphones, business processes or the web, machine learning needs to be deeply engrained into complex systems, work with heterogeneous and quickly changing data situations, and deliver results that can be processed seamlessly.
- Detecting patterns, with applications in fraud detection or decision support
- Detecting complex events in times series, with applications in sensor data and factories of the future
- Mining in big data architectures including stream mining
Mining complex data
In modeling real-world applications, often various dependencies need to be taken into account: friends linking to friends in social networks, the flows of goods in logistic networks along streets, tracks or flight lanes, or biological processes in the human body. These are just a few examples of data that can be represented adequately only in the form of graphs. Because of their complexity compared to tabular data, mining graphs and other highly structured objects is significantly more challenging than other forms of data mining. We investigate efficient methods to adequately extract knowledge out of graphs and network data.