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.
Facilitating cross-sectoral Big Data integration through efficient hybrid computational methods based on scalable Big Data algorithms coupled with human computation, interaction and feedback.
Supporting effective collaboration and feedback from diverse stakeholders: industry, public administration and citizens, to optimize existing services and introduce novel data-driven opportunities across European cities through crowdsourcing and open innovation management methods.
Validating results in two vertical Big Data value chains in the sectors of urban mobility and smart individual navigation, including mutual integration of road information and urban datasets, complemented with crowdsourced 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.
Two vertical data value chains using cross-sectoral data integration from the industrial and public domain built upon a mix of multilingual open and privately managed data, including text, structured data, social media streams, and rapidly-streamed sensor data.
Cross-sectoral and cross-lingual technology, including algorithms and tools covering the complete cross-sectoral Big Data Value Chain, capitalizing on a flexible and efficient combination of human and machine-based computation and relying on statistical and knowledge-driven data-inspection techniques.
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