Vector Databases for Modelling, Managing and Querying Big Scientific Data: Models, Issues, Paradigms

Inspired by the emergence of scientific data in fields like as astronomy, climate research, and genomics, which presents significant challenges for conventional database systems. This vision paper investigates the adaptation of vector databases in order to describe, handle, and query large-scale scientific data. Moreover, we propose a road-map for embedding-centric data infrastructures, along with an analysis of current advancements and identification of important future research directions, including explainability and scalability. This research provides a foundational basis for next-generation interdisciplinary scientific discovery.