A large number of critical infrastructure facilities are located in cities and their surroundings, providing essential services in a compact geographical space and resulting in mutual physical and logical dependencies. The provision of services such as electricity, gas, water, communication, food, fuel, road or rail, in particular, is achieved by operating extensive networks. In the FFG-funded project ODYSSEUS, Stefan Rass (Institute of Applied Informatics) and his team are working on developing a framework for a simulation designed to forecast the consequences of attacks on such interlinked infrastructure facilities.
Recommender systems represent a key technology for e-commerce providers such as Google, Amazon, Netflix, Booking.com and Spotify. It is therefore with a certain urgency that researchers are working intensively on making ever more accurate predictions about the products and services users might want to consume next. However, in a paper published recently, Maurizio Ferrari Dacrema, Paolo Cremonesi and Dietmar Jannach were able to show that several critical issues concerning the research methodology are hindering progress in the development of recommender systems. In recognition of their work, they received the Best Full Paper Award at the renowned ACM Conference on Recommender Systems in Copenhagen in September.