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.

Certificates play an important role in education and in professional development in companies. Individual learning records become essential for people’s professional careers. It is therefore important that these records are stored in long-term available and tamper-proof ledgers. A blockchain records transactions in a verifiable and permanent way, therefore it is very suitable to store fingerprints of certificates or other educational items. Blockchain reveals forgery of certificates and it supports learning histories. In this paper, we present the Blockchain for Education platform as a practical solution for issuing, validating and sharing of certificates. At first, we describe the conceptual system overview and then we present in detail the platform implementation including management of certification authorities and certificates, smart contracts as well as services for certifiers, learners and third parties such as employers. Finally, we describe use cases and first evaluation results that we gathered from end user tests with certifiers and conclude with a discussion.

False alarms triggered by sensors of alarm systems are a frequent and costly inconvenience for the emergency services and owners of alarm systems. Around 90% of false alarms are caused by either technical failures such as network down times or human error. To remedy this problem, we develop a novel alarm verification service by leveraging the power of an alarm data warehouse. In addition, we apply various machine learning algorithms to identify false alarms. The goal of our system is to help human responders in their decision about whether or not to trigger costly intervention forces.

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.