Imagine trying to find one specific needle in a mountain of different needles, where each needle is stored in a different type of box. This project builds a smart universal remote that lets you ask questions in plain English to find exactly what you need across all those boxes instantly. It connects giant supercomputers to these data piles so businesses can analyze massive amounts of information without getting bogged down by technical complexity.
Companies struggle to analyze massive, fragmented datasets stored across different database types. Current tools often cannot bridge the gap between raw storage and the immense power of supercomputers.
An Extreme Data Database (EDD) and an Advanced Query and Indexing System (AQIS) that uses LLMs to allow natural language queries across diverse data backends.
If you are an autonomous vehicle developer dealing with massive sensor logs and driving data — this project developed an Extreme Data Database (EDD) that allows you to query and analyze these datasets on supercomputers to speed up AI training.
If you are a smart viticulture provider dealing with heterogeneous environmental and crop data — this project developed a unified indexing system that streamlines data ingestion and analysis for better crop yield predictions.
If you are a health analytics firm dealing with social big data and patient records — this project developed a portable analytics environment that ensures FAIR data handling while enabling complex knowledge extraction.
Based on available project data, no specific pricing or cost model is mentioned; the project is funded by an EU contribution of EUR 4,911,425 for development.
Yes, it is specifically designed for 'Extreme Data' and integrates with European pre-Exascale Supercomputers and cloud infrastructures to process data at scale.
Based on available project data, specific licensing terms are not listed, but the project includes partners specialized in technology transfer to manage these aspects.
The system uses a unified interface called AQIS to connect with S3, iRODS, PostgreSQL, OpenSearch, ElasticSearch, Mongo, and vector or graph databases.
The project period runs from 2023-01-01 to 2026-04-30, indicating the development and validation phase is ongoing.
The consortium is heavily industry-weighted with a 60% industry ratio (6 out of 10 partners), including 2 SMEs. This strong commercial presence, combined with 3 universities and 1 research center, suggests the project is focused on practical application rather than pure theory, specifically targeting sectors like automotive (Valeo) and high-performance computing.
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