BCD.S+M

Goals

One of the biggest challenges for the coming decade lies in how we store and process data. As Artificial Intelligence (AI) systems continue to grow exponentially, their development increasingly depends on the ability to handle vast volumes of data, from ensuring data quality to managing its storage and processing. Despite advancements in AI and distributed systems, finding solutions that balance performance, security, and privacy at scale remains an open and pressing challenge.

The BCD.S+M project aims to address this gap by exploring new approaches to manage large volumes of data efficiently and securely, adapting dynamically to the data’s own characteristics and requirements. It seeks to rethink data storage management by targeting the core bottlenecks currently limiting system performance and scalability in data-centric applications.

The project’s research output will focus on advancing the scientific and technological frontier through high-impact publications. At the same time, BCD.S+M aims to demonstrate its relevance and performance not only in sensitive domains like healthcare but also in high-demand environments such as High-Performance Computing (HPC) infrastructures. The ultimate goal is to expand pilot use cases across institutions and convert these into production-ready deployments, paving the way for broader market adoption.

By combining the expertise of the software engineers at Invisible Lab with the highly skilled researchers in distributed storage at INESC TEC, the project will deliver innovative results that push forward the state of the art and bridge the gap between academic research and real-world impact.

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