Alberto Rosi

TL;DR: A probabilistic topic models approach to automatically extract urban patterns from location-based social network data is tested and it is found that the extracted patterns can identify hotspots in the city, and recognize a number of major crowd behaviors that recur over time and space in the urban scenario.

TL;DR: This paper explores how “social sensing” could be integrated into pervasive systems, and frame and survey the possible approaches to such an integration.

TL;DR: The nature-inspired coordination model developed within the SAPERE project is described as a synthesis of existing approaches, and it is shown how it can effectively support the multifold requirements of modern and emerging pervasive services.

TL;DR: This paper proposes a simple model for the representation of contextual information, the design and implementation of a general infrastructure for browsing the world, as well as some exemplar services it has implemented over it.

TL;DR: The overall objectives and approach of the SAPERE (“Self-aware Pervasive Service Ecosystems”) project are presented, focussed on the development of a highly-innovative nature-inspired framework suited for the decentralized deployment, execution, and management of self-aware and adaptive pervasive services in future network scenarios.