University of Twente Student Theses


Semantic model of the Computing Capacity matching within the FAIR Data Train

Gülbey, Baran (2023) Semantic model of the Computing Capacity matching within the FAIR Data Train.

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Abstract:In order to improve healthcare and science, patient data is essential. Especially because health data records are regarded as sensitive information. This is where the concept of FAIR Data Train (FDT) comes into play, where ‘Trains’ are algorithms that visit ‘Data Stations’ where health data is stored. By allowing the Train algorithms to access the data locally at each station, the FDT model promises safe and privacy-preserving data analysis. This research identifies the computing capabilities of Data Stations and computing requirements of Trains to support the evaluation of whether Data Stations are capable of running Trains. The relevant computing components are compiled into the ‘Computing Environment’, which describes the Data Station’s computing capabilities and the Train’s computing requirements. The Computing Environment and its various computing components form the basis for the design of an RDF semantic model, which is then validated against a set of conditions using the Shapes Constraint Language (SHACL). By achieving an accurate semantic model of the Computing Capacity matching, this work optimizes interoperability and thereby improves the overall efficiency of the FDT. The development of a semantic model for the Computing Environment of Trains and Data Stations will significantly contribute to the overall goals of the FAIR Data Train.
Item Type:Essay (Bachelor)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Business & IT BSc (56066)
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