Via Marco Biagi 5 – 73100 Lecce, Italy
(+39) 0832 1902411
Fabrizio Antonio was awarded a Master’s Degree with first-class honors in Computer Engineering from the University of Salento, Faculty of Engineering, in April 2016, with a thesis on High- Performance Computing and Big Data. In May 2016, he joined the Data Science and Learning Research Team within the Advanced Scientific Computing (ASC) Division of CMCC.
His research activities focus on distributed data management and high-performance data analytics and mining for eScience in the context of climate change.
In 2016, he started working on the MARSOP4 (Monitoring Agriculture with Remote Sensing OPerational – Lot 4) project, dealing with the architectural design and implementation of a new highly configurable caching system for the processing and visualization of the JRC MARS database data. He has cooperated with the CMCC Supercomputing Center staff in the management, maintenance and monitoring of the virtual infrastructure devoted to the MARSOP4 project (in VMware and Oracle environments), gaining specific experience as Oracle Database Administrator. In the context of the PRIMAVERA (PRocess-based climate sIMulation: AdVances in high resolution modelling and European climate Risk Assessment) project, he has been contributing to the publication of the CMCC-CM2 model datasets on the ESGF Data Node at CEDA.
Since 2016, he has also been involved in several national and international projects like BARRACUDA, NEXTDATA, APOLLON, “NEMO Evolution” Strategic Project, EOSC-Hub, working on data management topics and contributing to software developments related to the Ophidia Big Data analytics framework.
LATEST PUBLICATIONS
- A Graph Data Model-based Micro-Provenance Approach for Multi-level Provenance Exploration in End-to-End Climate Workflows
- A Data Space for Climate Science in the European Open Science Cloud
- Coordinating an operational data distribution network for CMIP6 data
- Towards High Performance Data Analytics for Climate Change
- Towards an Open (Data) Science Analytics-Hub for Reproducible Multi-Model Climate Analysis at Scale