DAMS: A Distributed Analytics Metadata Schema - Kölner UniversitätsPublikationsServer

Welten, Sascha, Neumann, Laurenz, Yediel, Yeliz Ucer, da Silva Santos, Luiz Olavo Bonino, Decker, Stefan and Beyan, Oya (2021). DAMS: A Distributed Analytics Metadata Schema. Data Intell., 3 (4). S. 528 - 548. CAMBRIDGE: MIT PRESS. ISSN 2641-435X

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DOI:10.1162/dint_a_00100

Abstract

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 data. These concepts propose privacy-enhancing alternatives to data centralisation approaches, which have restricted applicability in case of sensitive data due to ethical, legal or social aspects. Nevertheless, 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 tasks. To approach the mentioned problems, we propose a metadata schema for DA infrastructures, which provides a vocabulary to enrich the involved entities with descriptive semantics. We initially perform a requirement analysis with domain experts to reveal necessary metadata items, which represents the foundation of our schema. Afterwards, we transform the obtained domain expert knowledge into user stories and derive the most significant semantic content. In the final step, we enable machine-readability via RDF(S) and SHACL serialisations. We deploy our schema in a proof-of-concept monitoring dashboard to validate its contribution to the transparency of DA architectures. Additionally, we evaluate the schema's compliance with the FAIR principles. The evaluation shows that the schema succeeds in increasing transparency while being compliant with most of the FAIR principles. Because a common metadata model is critical for enhancing the compatibility between multiple DA infrastructures, our work lowers data access and analysis barriers. It represents an initial and infrastructure-independent foundation for the FAIRification of DA and the underlying scientific data management.

Item Type:

Journal Article

Creators:

Creators	Email	ORCID	ORCID Put Code
Welten, Sascha	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Neumann, Laurenz	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Yediel, Yeliz Ucer	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
da Silva Santos, Luiz Olavo Bonino	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Decker, Stefan	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED
Beyan, Oya	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED