News
Blog
DataNexus and EUDATA+: the clustering approach of Graph-Massivizer
With hundreds of projects funded by the European Commission that run more or less at the same time, activities that used to be rather easy in the past, have become a real nightmare for projects. Some of those activities are: i) understanding what other projects do and...
GraphMa: Public Release and What’s New
One year ago, we published a blog post introducing the concept of GraphMa within the Graph-Massivizer project. At that time, the GitHub repository was private, and we shared only conceptual insights and early design principles. During 2025, we made the GraphMa GitHub...
Navigating the Computing Continuum: Enabling Scalable and Sustainable Graph Processing
Rethinking Infrastructure Boundaries for Graph Analytics The Graph-Massivizer project reimagines how large-scale graph data is processed by leveraging the computing continuum, a seamless integration of edge, cloud, and high-performance computing (HPC) environments....
Synthetic Financial Data Generation: Engineering Market-Consistent Time Series for Quantitative Research, Trading, and Regulatory Compliance
Modern quantitative finance is increasingly constrained not by a lack of ideas, but by limitations in data availability, usability, and regulatory permissibility. As trading strategies, risk engines, and AI-driven models become more sophisticated, the demand for...
From Scarcity to Scale: How Synthetic Financial Data Is Powering AI Training for Quantitative Trading Strategies
Artificial intelligence has moved from experimentation to production across quantitative trading, portfolio construction, execution optimization, and risk management. However, while model architectures and compute capacity have scaled rapidly, the availability of...
How Temporal Shifting Affects the Carbon Intensity of Data-Centre Workloads (VU)
Processing large-scale graph-processing workloads requires similarly large-scale infrastructure, which we know today as data centres: large computing facilities, deploying hundreds or thousands of interconnected computers. Data centres form the basis of today’s...
From Complex Queries to Intelligent Assistants: How Graph-Powered AI is Transforming Data Center Operations (UNIBO)
Innovations from the University of Bologna and CINECA in the Graph-Massivizer Project Authors: Prof.Andrea Bartolini (Associate Professor at University of Bologna) and Junaid Ahmed Khan (PhD student and Research Fellow at the University of Bologna) Modern data centers...
AI for Massive Knowledge Graphs
Integrating knowledge graph technologies with AI agents and graph analytics can expose new pathways to interacting with your data and extract actionable information for your use case, allowing even massive graphs to be processed in a scalable manner. Here, we explore...
AI and Sustainability: Bridging Innovation and Environmental Responsibility
AI and Sustainability: Bridging Innovation and Environmental Responsibility Artificial intelligence is reshaping modern society, but this technological revolution comes at an environmental cost that we can no longer ignore. Although AI offers innovative ways to tackle...
GraphMa: Graph Processing with Pipeline-Oriented Computation
In the realm of data science and analytics, the significance of graph processing cannot be overstated. The intricate web of relationships and connections that graphs represent are fundamental to understanding complex systems, from social networks to biological...
News
Blog
DataNexus and EUDATA+: the clustering approach of Graph-Massivizer
With hundreds of projects funded by the European Commission that run more or less at the same time, activities that used to be rather easy in the past, have become a real nightmare for projects. Some of those activities are: i) understanding what other projects do and...
GraphMa: Public Release and What’s New
One year ago, we published a blog post introducing the concept of GraphMa within the Graph-Massivizer project. At that time, the GitHub repository was private, and we shared only conceptual insights and early design principles. During 2025, we made the GraphMa GitHub...
Navigating the Computing Continuum: Enabling Scalable and Sustainable Graph Processing
Rethinking Infrastructure Boundaries for Graph Analytics The Graph-Massivizer project reimagines how large-scale graph data is processed by leveraging the computing continuum, a seamless integration of edge, cloud, and high-performance computing (HPC) environments....
Synthetic Financial Data Generation: Engineering Market-Consistent Time Series for Quantitative Research, Trading, and Regulatory Compliance
Modern quantitative finance is increasingly constrained not by a lack of ideas, but by limitations in data availability, usability, and regulatory permissibility. As trading strategies, risk engines, and AI-driven models become more sophisticated, the demand for...
From Scarcity to Scale: How Synthetic Financial Data Is Powering AI Training for Quantitative Trading Strategies
Artificial intelligence has moved from experimentation to production across quantitative trading, portfolio construction, execution optimization, and risk management. However, while model architectures and compute capacity have scaled rapidly, the availability of...
How Temporal Shifting Affects the Carbon Intensity of Data-Centre Workloads (VU)
Processing large-scale graph-processing workloads requires similarly large-scale infrastructure, which we know today as data centres: large computing facilities, deploying hundreds or thousands of interconnected computers. Data centres form the basis of today’s...
From Complex Queries to Intelligent Assistants: How Graph-Powered AI is Transforming Data Center Operations (UNIBO)
Innovations from the University of Bologna and CINECA in the Graph-Massivizer Project Authors: Prof.Andrea Bartolini (Associate Professor at University of Bologna) and Junaid Ahmed Khan (PhD student and Research Fellow at the University of Bologna) Modern data centers...
AI for Massive Knowledge Graphs
Integrating knowledge graph technologies with AI agents and graph analytics can expose new pathways to interacting with your data and extract actionable information for your use case, allowing even massive graphs to be processed in a scalable manner. Here, we explore...
AI and Sustainability: Bridging Innovation and Environmental Responsibility
AI and Sustainability: Bridging Innovation and Environmental Responsibility Artificial intelligence is reshaping modern society, but this technological revolution comes at an environmental cost that we can no longer ignore. Although AI offers innovative ways to tackle...
GraphMa: Graph Processing with Pipeline-Oriented Computation
In the realm of data science and analytics, the significance of graph processing cannot be overstated. The intricate web of relationships and connections that graphs represent are fundamental to understanding complex systems, from social networks to biological...
