Name: Aviation -driven Data Value Chain for Diversified Global and Local Operations
Funding_Authority: EU-H2020
Coordinator: UBITECH-Gioumpitek Meleti Schediasmos Ylopoiisi kai Polisi Ergon Pliroforikis Etaireia Periorismenis Efthynis
Start_Date: 01/01/2018
End_Date: 31/12/2020
Duration(months): 36
UCY_Budget: 278,750
Total_Budget: 3,951,125
Principal_Investigator: Pallis George
Co_principal_Investigator: Dikaiakos Marios


Current Projects ICARUS - Aviation-driven Data Value Chain for Diversified Global and Local Operations ICARUS aims to build a novel data value chain in the aviation-related sectors towards data-driven innovation and collaboration across currently diversified and fragmented industry players, acting as multiplier of the “combined” data value that can be accrued, shared and traded, and rejuvenating the existing, increasingly non-linear models / processes in aviation. Using methods such as big data analytics, deep learning, semantic data enrichment, and blockchain powered data sharing, ICARUS will address critical barriers for the adoption of Big Data in the aviation industry (e.g. data fragmentation, data provenance, data licensing and ownership, data veracity), and will enable aviation-related big data scenarios for EU-based companies, organizations and scientists, through a multi-sided platform that will allow exploration, curation, integration and deep analysis of original, synthesized and derivative data characterized by different velocity, variety and volume in a trusted and fair manner. ICARUS will bring together the Aerospace, Tourism, Health, Security, Transport, Retail, Weather, and Public sectors and accelerate their data-driven collaboration under the prism of a novel aviation-driven data value chain. Representative use cases of the overall domain’s value chain include: (I) Sophisticated passenger handling mechanisms and personalised services on ground facilities, (II) Enhanced routes analysis of aircrafts for improved fuel consumption optimisation and pollution awareness, (III) More accurate and realistic prediction model of epidemics, (IV) Novel Passenger experiences pre-in- and post-flight.

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