These courses are taught in a blended way (face-to-face and online) by EDSA partners and associate EDSA partners.
The objective of this course is to deeper understand and study the behavior of the networks arising within distributed systems. In particular, the course will focus on the concepts of graph theory which will allow to explain the connectivity and dynamics of many real world networks. The course will also cover the topics of Distributed Data Management, Large Graph Processing, Publish/Subscribe Systems, Navigable Small-World Overlays.
The blended learning course Data Scientist for Smart Buildings deals with methods and software for intelligent energy management (monitoring, analysis, simulation, optimization) in buildings or infrastructure based on measurement data resources.
The European power grid is facing massive challenges. The increasing amount of distributed renewable energy generation overthrows the known concepts of producers and consumers in the energy market - consumers turn to producers, traditional producers loosing their key incoming sources and turning towards service providers. Volatile energy sources like wind and solar require the power grid to turn smart, in-order to keep the grid stability facing bottom-up energy and volatile flows. Hence, ICT is playing an increasingly important role steering energy flows and forming the power grid of the future.
This course describes the critical technology trends that are enabling cloud computing and the services and applications they offer. The course covers a wide variety of advanced topics in data intensive computing, including distributed file systems, NoSQL databases, processing data-at-rest (batch data) and data-in-motion (streaming data), graph processing, and resource management. The course is mainly based on research papers.
This course introduces fundamental principles and techniques of Distributed Artificial Intelligence (DAI), as well as the usage of such techniques for creating applications in distributed computing environments. Central to the course are the concepts of "intelligent agents", as a paradigm for creating autonomous software components, and “multi-agent systems” as a way of providing coordination and communication between individual autonomous software components.
The main objective of this course is to provide the students with a solid foundation for understanding, analyzing and designing distributed algorithms for reliable distributed systems.
Being able to process large amounts of data in a timely manner is not enough in order to be able to develop big data solutions. Every successful implementation must meet minimum requirements regarding security and data protection. This seminar imparts the necessary security competences: The seminar will increase the participants‘ awareness concerning security and data protection and they will acquire basic knowledge about how to use security solutions in big data environments.