Graph Massivizer

Extreme and Sustainable Graph Processing for Urgent Societal Challenges in Europe

Basic information

Call: HORIZON-CL4-2022-DATA-01
Grant agreement: 101093202
Type of Action: Horizon Research and Innovation Actions
Granting Authority: European Commission
Duration: 1.1.2023 – 31.12.2025

Website: https://graph-massivizer.eu/

About

Graph-Massivizer focuses on developing a high-performance, scalable, and sustainable platform for processing extreme data through massive graph representations. The project aims to create a toolkit of open-source software tools and FAIR graph datasets that cover the entire lifecycle of processing extreme data, from ingestion and graph creation to automated analytics and reasoning. The platform is designed to be usable across a broad spectrum of stakeholders, from large enterprises to smaller organizations, using serverless computing paradigms to optimize performance and sustainability. Graph-Massivizer will validate its approach through four real-world use cases, demonstrating significant improvements in data analytics and energy efficiency.

What is the problem the project is addressing?

As data volumes continue to grow, particularly in critical sectors such as green finance, environmental protection, and the automotive industry, there is a pressing need for more efficient and sustainable ways to process and analyze extreme data. Current solutions are often inefficient, with high energy consumption and limited scalability. Graph-Massivizer addresses this challenge by developing advanced graph processing technologies that can handle massive datasets in a sustainable way, improving both performance and energy efficiency.

How is it solving the problem?

Graph-Massivizer focuses on creating a platform that processes extreme data using massive graphs. It provides five open-source software tools that enable stakeholders to process data efficiently, with a focus on sustainability. These tools support the entire data lifecycle, from data ingestion and graph creation to automated analytics and reasoning. The project uses serverless computing to optimize operations, and it applies environmental sustainability models to ensure that the platform reduces energy consumption. Through its four use cases, the project will demonstrate significant improvements in analytics performance and energy efficiency.

Expected Tangible results

Key outcomes include:

A scalable and sustainable platform for processing extreme data using massive graph representations.
Five open-source tools that support the entire data lifecycle, from data ingestion to automated intelligence.
70% more efficient analytics compared to current technologies like AliGraph.
30% improvement in energy awareness for ETL storage operations compared to Amazon Redshift.
A two-fold increase in data center energy efficiency and over 25% reduction in GHG emissions for basic graph operations.
Validation through four use cases that cover sustainable green finance, global environmental protection, green AI in the automotive industry, and data center digital twins for exascale computing.

PROJECT PARTNERS

Graph-Massivizer is a collaborative effort of 12 partners from eight countries, including four academic universities, two applied research centers, one high-performance computing (HPC) center, two SMEs, and two large enterprises. The project leverages the expertise of leading European researchers in graph processing and serverless computing, as well as European infrastructure in the computing continuum. The partners bring a wealth of knowledge in both the academic and industrial fields, ensuring the project’s success in developing and deploying this innovative platform. For more information, visit the official Graph Massivizer website.

CONTACT

International Research Centre
on Artificial Intelligence (IRCAI)
under the auspices of UNESCO

Jožef Stefan Institute
Jamova cesta 39
SI-1000 Ljubljana

info@ircai.org
ircai.org

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