Exploring Architectural Solutions for Implementing the FAIR Principles in Big Data Environments

• João P. C. Castro University of São Paulo (USP) / Federal University of Minas Gerais (UFMG)
• Cristina D. Aguiar University of São Paulo (USP)

DOI: https://doi.org/10.5753/sbbd_estendido.2023.232886

Abstract

The concept of Open Science has emerged as a major enabler for scientific collaboration. To develop repositories adhering to this concept, the FAIR Principles have been proposed. However, fulfilling these principles can be challenging when dealing with a significant volume, variety, and velocity of data and metadata. A suitable solution is to develop a Software Reference Architecture (SRA) that considers the characteristics of big data environments and the FAIR Principles. Despite the importance of this solution for Open Science, existing literature lacks a big data SRA that achieves full FAIR compliance. In our research, we address this gap by proposing architectural solutions for the implementation of FAIR-compliant big data sharing repositories. We validate these solutions through case studies and performance evaluations. Future contributions include developing algorithms to instantiate the proposed architectures and creating FAIR-compliant artificial datasets to assist in further validations.

Keywords: Open science, PAIR principles, Software reference Architecture

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