An Open System for Social Computation

Part of the power of social computation comes from using the collective intelligence of humans to tame the aggregate uncertainty of (otherwise) low veracity data obtained from human and automated sources. We have witnessed a surge in development of social computing systems but, ironically, there have been few attempts to generalise across this activity so that creation of the underlying mechanisms themselves can be made more social. We describe a method for achieving this by standardising patterns of social computation via lightweight formal specifications (we call these social artifacts) that can be connected to existing internet architectures via a single model of computation. Upon this framework we build a mechanism for extracting provenance meta-data across social computations

The RainBO Platform for Enhancing Urban Resilience to Floods: An Ecient Tool for Planning and Emergency Phases

Many urban areas face an increasing flood risk, which includes the risk of flash floods. Increasing extreme precipitation events will likely lead to greater human and economic losses unless reliable and efficient early warning systems (EWS) along with other adaptation actions are put in place in urban areas. The challenge is in the integration and analysis in time and space of the environmental, meteorological, and territorial data from multiple sources needed to build up EWS able to provide efficient contribution to increase the resilience of vulnerable and exposed urban communities to flooding. Efficient EWS contribute to the preparedness phase of the disaster cycle but could also be relevant in the planning of the emergency phase. The RainBO Life project addressed this matter, focusing on the improvement of knowledge, methods, and tools for the monitoring and forecast of extreme precipitation events and the assessment of the associated flood risk for small and medium watercourses in urban areas. To put this into practice, RainBO developed a webGIS platform, which contributes to the “planning” of the management of river flood events through the use of detailed data and flood risk/vulnerability maps, and the “event management” with real-time monitoring/forecast of the events through the collection of observed data from real sensors, estimated/forecasted data from hydrologic models as well as qualitative data collected through a crowdsourcing app