The BigFAIR Architecture: Enabling Big Data Analytics in FAIR-compliant Repositories

Collaboration is essential for scientific research. This is the foundation behind Open Science and the FAIR Principles, aimed at standardizing the development of scientific data sharing repositories. However, developing FAIR-compliant repositories can be a challenge, mostly due to managing a huge volume and variety of research data and metadata generated at a high velocity. We address these challenges by proposing BigFAIR, a novel FAIR-compliant architecture capable of managing this type of information in a massive scale. BigFAIR leverages existing local repositories, using separate infrastructures to handle scientific data and metadata. With this separation, it can reduce development and maintenance efforts, support data ownership, and increase flexibility. We define pipelines to demonstrate how BigFAIR answers queries in different contexts, introduce guidelines to support its instantiation, and propose a generic metadata warehouse model to support analytical query processing. We also demonstrate the applicability of BigFAIR through a case study in the context of two real-world datasets, detailing different types of queries and highlighting their importance to big data analytics.