News
Blog
Synthetic Data Powered Investment and Trading
Author: Laurentiu Vasiliu Peracton Ltd. In the ever-evolving world of finance, Synthetic Data Driven Investment and Trading is emerging as hybrid approach, where financial algorithms are not only powered by traditional financial data (historic and live), but also by...
Trading in the Matrix
Author: Laurentiu Vasiliu Peracton Ltd. Imagine a scenario where extreme quantities of synthetic data are continuously generated and used to train multiple generations of AI enhanced financial algorithms. In this scenario, financial algorithms train on making...
Building massive knowledge graphs using automated ETL pipelines
In this blog post, written by our colleagues Wolfgang Schell and Pauline Lencio at metaphacts , the Graph-Massivizer team explains how to build a massive knowledge graph from existing information or external sources in a repeatable and scalable manner. The post...
From Big Data to Green Data: Reducing the Environmental Impact of Data Science with Graph Massivizer
Data analysis and data processing are technologies that are increasingly prevalent in everyday life, with applications in research, industry, commerce, and public administration. However, they also have a significant environmental impact, both direct and indirect, due...
The importance of the semantic knowledge graph
This article is the first in a series of two where our partner metaphact presents a perspective on what is considered a semantic knowledge graph, why it's important (specifically in the context of AI and LLMs) and reflect on how they can drive the enterprises' goals...
Unveiling The Future: How Event Registry Utilizes Graph-Massivizer to Fuel UN Sustainable Development Goals
EVENTREGISTRY Unveiling The Future: How Event Registry Utilizes Graph-Massivizer to Fuel UN Sustainable Development Goals The Pioneering Spirit of Graph-Massivizer Project In a world drowning in data, the need for advanced processing capabilities has never been more...
Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 2)
Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 2)The preceding blog (part 1) highlighted the challenges posed by historical data, encompassing limitations related to data...
Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 1)
Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 1)Artificial intelligence (AI) has revolutionized the field of financial trading and investment. AI models can analyze...
M100 ExaData: a data collection campaign on the CINECA’s Marconi100 Tier-0 supercomputer
The creation of the first holistic dataset of a tier-0 Top10 supercomputer which will serve also to Graph-Massivizer projectWe are pleased to announce the publication of a new paper by our esteemed partners at University of Bologna, which showcases the valuable...
PRESS RELEASE: Graph-Massivizer promotes climate-neutral and sustainable economic sectors boosted by graph data processing
The Graph-Massivizer project consortium is happy to announce the official start of this European initiative, funded by the European Commission under the Horizon Europe research and innovation programme. Graph-Massivizer aims at delivering open-source and commercial...
News
Blog
Synthetic Data Powered Investment and Trading
Author: Laurentiu Vasiliu Peracton Ltd. In the ever-evolving world of finance, Synthetic Data Driven Investment and Trading is emerging as hybrid approach, where financial algorithms are not only powered by traditional financial data (historic and live), but also by...
Trading in the Matrix
Author: Laurentiu Vasiliu Peracton Ltd. Imagine a scenario where extreme quantities of synthetic data are continuously generated and used to train multiple generations of AI enhanced financial algorithms. In this scenario, financial algorithms train on making...
Building massive knowledge graphs using automated ETL pipelines
In this blog post, written by our colleagues Wolfgang Schell and Pauline Lencio at metaphacts , the Graph-Massivizer team explains how to build a massive knowledge graph from existing information or external sources in a repeatable and scalable manner. The post...
From Big Data to Green Data: Reducing the Environmental Impact of Data Science with Graph Massivizer
Data analysis and data processing are technologies that are increasingly prevalent in everyday life, with applications in research, industry, commerce, and public administration. However, they also have a significant environmental impact, both direct and indirect, due...
The importance of the semantic knowledge graph
This article is the first in a series of two where our partner metaphact presents a perspective on what is considered a semantic knowledge graph, why it's important (specifically in the context of AI and LLMs) and reflect on how they can drive the enterprises' goals...
Unveiling The Future: How Event Registry Utilizes Graph-Massivizer to Fuel UN Sustainable Development Goals
EVENTREGISTRY Unveiling The Future: How Event Registry Utilizes Graph-Massivizer to Fuel UN Sustainable Development Goals The Pioneering Spirit of Graph-Massivizer Project In a world drowning in data, the need for advanced processing capabilities has never been more...
Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 2)
Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 2)The preceding blog (part 1) highlighted the challenges posed by historical data, encompassing limitations related to data...
Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 1)
Why Current Financial Historic Data is Neither Enough nor Truly Useful for Testing Financial Trading and Investment Models in the AI Era (part 1)Artificial intelligence (AI) has revolutionized the field of financial trading and investment. AI models can analyze...
M100 ExaData: a data collection campaign on the CINECA’s Marconi100 Tier-0 supercomputer
The creation of the first holistic dataset of a tier-0 Top10 supercomputer which will serve also to Graph-Massivizer projectWe are pleased to announce the publication of a new paper by our esteemed partners at University of Bologna, which showcases the valuable...
PRESS RELEASE: Graph-Massivizer promotes climate-neutral and sustainable economic sectors boosted by graph data processing
The Graph-Massivizer project consortium is happy to announce the official start of this European initiative, funded by the European Commission under the Horizon Europe research and innovation programme. Graph-Massivizer aims at delivering open-source and commercial...