Topics

The research group develops scalable analytics algorithms based on Apache Spark and Apache Flink for analyzing large scale datasets by distributing the computational tasks in memory.

The group focuses on machine learning over knowledge graphs, in particular by computing embeddings and by learning description logic concepts.

The use of Semantic Web technologies led to an increasing number of structured data published on the Web. Despite the advances in question answering systems retrieving the desired information from structured sources is still a substantial challenge.

The SEEDS group is dedicated to the cross-fertilization of software engineering, data science and machine learning:

• Software engineering for Data Science addresses the use of software engineering techniques for improving the ease of use, predictabilty, reliability and robustness of current data analytics and machine learning tools, languages and frameworks. Our Simple-ML project, dedicated to the development of an easy to use domain-specific language for data analytics, exemplifies this direction.
• Data Science for Software Engineering investigates how machine learning / data science can provide new solutions to classic software engineering problems such as software quality analysis, fault detection, program comprehension, human computer / computer human interaction, etc., advancing the way in which software is designed, synthetised and assessed.