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UBITECH presents a scientific paper on Big Data Technologies and Applications at EAI BDTA 2020

The scientific paper entitled ”A big data intelligence marketplace and secure analytics experimentation platform for the aviation industry” has been accepted and presented at the 10th EAI International Conference on Big Data Technologies and Applications, which was held online worldwide on the 11th of December 2020. Mr Dimitris Miltiadou, delivery manager of the Data Science and Analytics research group of UBITECH, and his co-authors presented the architecture of the ICARUS big data-enabled platform that holistically handles the complete big data lifecycle from the data collection, data curation and data exploration to the data integration and data analysis of data originating from heterogeneous data sources with different velocity, variety and volume in a trusted and secure manner.

The designed architecture is a modular architecture, composed by 22 components in total, that is designed aiming to offer the maximum flexibility and extensibility, enabling the smooth integration and effective operation of the various components that are implemented as distinct software modules. Moreover, the architecture incorporates all the entire lifecycle of the platform that spans from data preparation and data upload, to data exploration, data sharing, data brokerage and data analysis.

Furthermore, to cope with the security and privacy constraints of the aviation sector, the platform adopts a security and privacy by-design approach that covers all the data confidentially, data privacy and data safeguarding aspects of the platform. The main axes of this approach is the end-to-end encryption of all the datasets that are available in the platform with a symmetric encryption key and the design and employment of a sophisticated secure decryption process with a decryption key in the form of key pair for each dataset per data consumer to facilitate the secure data sharing of the datasets across all the platform.

The paper is available in the EAI BDTA 2020 conference proceedings at https://link.springer.com/chapter/10.1007/978-3-030-72802-1_4