We introduce a Clinical Decision Support System (CDSS) as an operation of translational medicine. It is based on random forests, is personalisable and allows a clear insight into the decision making process. A well-structured rule set is created and every rule of the decision making process can be observed by the user (physician). Furthermore, the user has an impact on the creation of the final rule set and the algorithm allows the comparison of different diseases as well as regional differences in the same disease.

There exists a multitude of implemented, as well as envisioned, use cases for the Internet of Things (IoT) and Wireless Sensor Networks (WSN). Some of these use cases would benefit from the collected data being globally accessible to: (a) authorized users only; and (b) data processing units through the Internet. Much of the data collected, such as location or personal identifiers, are of a highly sensitive nature. Even seemingly innocuous data (e.g., energy consumption) can lead to potential infringements of user privacy.

In recent years large amounts of data have been made publicly available: literally thousands of open data sources exist, with genome data, temperature measurements, stock market prices, population and income statistics etc. However, accessing and combining data from different data sources is both non-trivial and very time consuming. These tasks typically take up to 80% of the time of data scientists. Automatic integration and curation of open data can facilitate this process.