Imagine trying to map every single connection in a giant web of billions of points, like every transaction in the global economy or every part in a car. This project builds a super-efficient engine that can handle these massive webs without crashing or wasting huge amounts of electricity. It's like upgrading from a paper map to a live, intelligent GPS that works instantly across the entire planet.
Companies struggle to process 'extreme data'—datasets with billions of connections—without incurring massive energy costs or suffering from slow performance. Current tools often fail to scale or are too energy-intensive for sustainable corporate goals.
A toolkit of five open-source software tools and FAIR graph datasets for the full lifecycle of extreme data processing.
If you are a financial institution dealing with complex fraud detection and green finance tracking — this project developed a toolkit that supports billions of vertices and trillions of edges. This allows for deeper reasoning across massive datasets to identify sustainable investments.
If you are a car maker dealing with the complexity of green AI for sustainable production — this project developed a processing platform that promises 70% more efficient analytics than AliGraph. This helps optimize the sustainable lifecycle of vehicle components.
If you are a cloud provider dealing with high energy costs for data storage — this project developed a system that aims for 30% improved energy awareness for ETL operations compared to Amazon Redshift. This can lead to a two-fold improvement in data center energy efficiency.
Based on available project data, the toolkit consists of five open-source software tools, suggesting the core technology is available without a direct purchase price.
Yes, the system is designed for extreme data, supporting up to billions of vertices and trillions of edges.
The project delivers five open-source software (OSS) tools and FAIR graph datasets.
It uses a serverless computing model and is designed to work across the computing continuum, including HPC systems.
The project period runs from 2023-01-01 to 2025-12-31.
The project is highly industry-oriented with a 46% industry ratio, comprising 6 industrial partners (including 2 SMEs) and 7 academic/research entities. This balance, spanning 8 countries, ensures that the high-performance computing research is grounded in practical applications for the automotive and finance sectors.
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