We aim to integrate geographic, transport, meteorological, cross-domain and news data, in order to capitalize on hybrid Big Data integration and analytics methods, while efficiently combining algorithms and human computation incorporated in the entire Big Data Value Chain.
Design and deploy efficient hybrid computational methods based on scalable Big Data algorithms coupled with human computation, interaction and feedback.
Collect and acquire heterogeneous data sources, including data from crowdworkers, citizens and their mobile phones.
Use the wisdom of the crowds to validate analytics on top of collected data.
QROWD services flexibly combining efficient and scalable algorithms and configurable crowdsourcing services (paid microtasks, gamification methods, open challenges) with social networks as an integral part of the QROWD data integration platform.
QROWD was used to implement vertical data value chains useful for both the industrial and public sector, tackling real-world needs of Cities and powering the development of new products and services.
Data Protection by Design
QROWD introduces data protection and privacy by design, easing compliance with General Data Protection Regulations.
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