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DAMS:A Distributed Analytics Metadata Schema

Data Intelligence 2021年第4期3卷 528-547页

作者： Sascha Welten laurenz neumann Yeliz Ucer Yediel Luiz Olavo Bonino da Silva Santos Stefan Decker Oya Beyan Chair Informatik 5 RWTH Aachen University52056 AachenGermany Fraunhofer Institute for Applied Information Techniques(FIT) 53757 Sankt AugustinGermany Faculty of Electrical Engineering Mathematics and Computer ScienceUniversity of Twente7500AE EnschedeThe Netherlands Department of Human Genetics Leiden University Medical CentreLeiden 2333 ZAThe Netherlands Institute of Medical Information Faculty of Medicine&University Hospital CologneUniversity of Cologne50674 CologneGermany

In recent years,implementations enabling Distributed Analytics(DA)have gained considerable attention due to their ability to perform complex analysis tasks on decentralised data by bringing the analysis to the *** concepts propose privacy-enhancing alternatives to data centralisation approaches,which have restricted applicability in case of sensitive data due to ethical,legal or social ***,the immanent problem of DA-enabling architectures is the black-box-alike behaviour of the highly distributed components originating from the lack of semantically enriched descriptions,particularly the absence of basic metadata for data sets or analysis *** approach the mentioned problems,we propose a metadata schema for DA infrastructures,which provides a vocabulary to enrich the involved entities with descriptive *** initially perform a requirement analysis with domain experts to reveal necessary metadata items,which represents the foundation of our ***,we transform the obtained domain expert knowledge into user stories and derive the most significant semantic *** the final step,we enable machine-readability via RDF(S)and SHACL *** deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA ***,we evaluate the schema’s compliance with the FAIR *** evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR *** a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures,our work lowers data access and analysis *** represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.

关键词： Distributed analytics Federated analytics Personal Health Train Metadata schema RDF(S) SHACL FAIR

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