Imagine if your smart devices only sent the absolute smallest bit of necessary info to the cloud instead of everything, like sending a text summary instead of a full video. This project builds a smart map that decides exactly where data should live and be processed to save electricity. It keeps your private information safe while making sure the system doesn't waste power moving data back and forth.
Big data analytics at the edge and cloud consume excessive energy, straining national grids and increasing carbon emissions. Current storage methods are inefficient because they move too much data and ignore privacy needs.
A Distributed Knowledge Graph (DKG) and a Metadata tool. These components optimize where data is stored and processed to reduce energy use.
If you are a factory operator dealing with massive amounts of sensor data and high energy bills — this project developed a Distributed Knowledge Graph that reduces power consumption by optimizing where analytics are performed. This minimizes data movement and lowers carbon emissions.
If you are a public agency dealing with strict citizen privacy laws and inefficient data silos — this project developed a metadata tool that incorporates trust and privacy into data operations. It ensures administrative confidentiality across edge and cloud systems.
If you are a grid operator dealing with volatile data from distributed energy resources — this project developed AI-driven data placement to minimize latency and energy use. This helps maintain grid stability without overloading national power networks.
Based on available project data, specific pricing or implementation costs are not provided.
Yes, the project is demonstrated on three industry settings covering public-service, manufacturing, and energy analytics to ensure industrial relevance.
Based on available project data, specific licensing terms are not mentioned, though the consortium includes 6 industry partners and 2 SMEs.
Yes, the project includes a Metadata tool specifically designed to incorporate privacy and trust aspects into data operations.
Based on available project data, the project follows a three-phase deployment from basic components to advanced components over a 3-year period.
The project features a strong industrial base with 6 industry partners and 2 SMEs, representing a 38% industry ratio. This balance between 6 universities and 6 industry players across 9 countries suggests the technology is being developed with direct commercial application in mind, rather than as a purely academic exercise.
Contact the Ministero dell'Economia e delle Finanze (Italy)
Contact us to explore licensing for the Distributed Knowledge Graph components.
