Designing a Trusted Data Brokerage Framework in the Aviation Domain

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 568)


In recent years, there is growing interest in the ways the European aviation industry can leverage the multi-source data fusion towards augmented domain intelligence. However, privacy, legal and organisational policies together with technical limitations, hinder data sharing and, thus, its benefits. The current paper presents the ICARUS data policy and assets brokerage framework, which aims to (a) formalise the data attributes and qualities that affect how aviation data assets can be shared and handled subsequently to their acquisition, including licenses, IPR, characterisation of sensitivity and privacy risks, and (b) enable the creation of machine-processable data contracts for the aviation industry. This involves expressing contractual terms pertaining to data trading agreements into a machine-processable language and supporting the diverse interactions among stakeholders in aviation data sharing scenarios through a trusted and robust system based on the Ethereum platform.


Data brokerage Collaboration platform Aviation 



This work has been created in the context of the ICARUS project, that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 780792.


  1. 1.
    Maheshwari, A., Davendralingam, N., DeLaurentis, D.A.: A comparative study of machine learning techniques for aviation applications. In: Aviation Technology, Integration, and Operations Conference, p. 3980 (2018)Google Scholar
  2. 2.
    Ariyawansa, C.M., Aponso, A.C.: Review on state of art data mining and machine learning techniques for intelligent airport systems. In: 2016 2nd International Conference Information Management (ICIM), pp. 134–138. IEEE (2016)Google Scholar
  3. 3.
    Gavrilovski, A., et al.: Challenges and opportunities in flight data mining: a review of the state of the art. In: AIAA Infotech@ Aerospace, p. 0923 (2016)Google Scholar
  4. 4.
    Camarinha-Matos, L.M., Afsarmanesh, H., Ollus, M.: ECOLEAD and CNO base concepts. In: Camarinha-Matos, L.M., Afsarmanesh, H., Ollus, M. (eds.) Methods and Tools for Collaborative Networked Organizations, pp. 3–32. Springer, Boston (2008). Scholar
  5. 5.
    Facca, F.M., Komazec, S., Guglielmina, C., Gusmeroli, S.: COIN: platform and services for SaaS in enterprise interoperability and enterprise collaboration. In: 2009 IEEE International Conference on Semantic Computing, pp. 543–550. IEEE (2009)Google Scholar
  6. 6.
    Biliri, E., et al.: Big data analytics in public safety and personal security: challenges and potential. In: 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC), pp. 1382–1386. IEEE (2017)Google Scholar
  7. 7.
    Camarinha-Matos, L.M., Macedo, P., Ferrada, F., Oliveira, A.I.: Collaborative business scenarios in a service-enhanced products ecosystem. In: Camarinha-Matos, L.M., Xu, L., Afsarmanesh, H. (eds.) PRO-VE 2012. IAICT, vol. 380, pp. 13–25. Springer, Heidelberg (2012). Scholar
  8. 8.
  9. 9.
    Performance Success Stories - Data Sharing Helps Airlines Reduce Delays.
  10. 10.
    EasyJet and Gatwick Launch Mobile Host to Simplify Airport Experience.
  11. 11.
    Data Sharing Transformation Driving Kerb to Gate Improvements at Dublin Airport.
  12. 12.
    Grabus, S., Greenberg, J.: Toward a metadata framework for sharing sensitive and closed data: an analysis of data sharing agreement attributes. In: Garoufallou, E., Virkus, S., Siatri, R., Koutsomiha, D. (eds.) MTSR 2017. CCIS, vol. 755, pp. 300–311. Springer, Cham (2017). Scholar
  13. 13.
    Cao, T.D., Pham, T.V., Vu, Q.H., Truong, H.L., Le, D.H., Dustdar, S.: MARSA: a marketplace for realtime human sensing data. ACM Trans. Internet Technol. (TOIT) 16(3), 16 (2016)CrossRefGoogle Scholar
  14. 14.
    Koutroumpis, P., Leiponen, A., Thomas, L.: The (unfulfilled) potential of data marketplaces, vol 53. The Research Institute of the Finnish Economy (2017)Google Scholar
  15. 15.
    Vu, Q.H., Pham, T.V., Truong, H.L., Dustdar, S., Asal, R.: DEMODS: a description model for data-as-a-service. In: 2012 IEEE 26th International Conference on Advanced Information Networking and Applications, pp. 605–612. IEEE (2012)Google Scholar
  16. 16.
  17. 17.
  18. 18.
    Özyilmaz, K.R., Doğan, M., Yurdakul, A.: IDMoB: IoT data marketplace on blockchain. In: 2018 Crypto Valley Conference on Blockchain Technology (CVCBT), pp. 11–19. IEEE, June 2018Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Suite5 Data Intelligence Solutions LimitedLimassolCyprus
  2. 2.Singularlogic Anonymi Etaireia Pliroforiakon Systimaton Kai Efarmogon PliroforikisKifisiaGreece
  3. 3.UBITECHChalandriGreece

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