Summary
The 4th industrial revolution is ongoing. The manufacturing and process industry have started transitioning into automated and data exchange operations. Still, the challenge for these players is to handle the massive amounts of data that is produced in large-scale real-time operations –and quite often– needs to be managed under time-pressure. As of now, the process industry has to choose between implementing large-scale analytics systems (using a scale-out or scale-up architecture) to control real-time operations and find metrics for predictions or alternatively, rely on machine learning (ML) technologies to discover meaning in data sets. At AIA Science –a spin-off from Numascale, world leader in high-computing performance or HPC– we have created AIA SGA, the first platform that integrates in a single solution a scale-up graph analytics architecture (based on HPC hardware) and the most advanced ML technology, allowing to automate real-time operations, whilst optimizing production and using anomaly detection to prevent accidents (e.g. the Deepwater Horizon oil spill disaster). Our current prototype (TRL6), combines three main components: The AIA Analytics engine; the AIA ML Suite and Neo4j Graph database software. Our priority market will be the European oil/gas and aluminium industries, having already a solid network of partners within our home market (e.g. Statoil, Aker BP or Norsk Hydro), where most European oil rigs and aluminium plants are concentrated. The market of ML-based large-scale analytics is gaining traction, being expected to be worth €1.1 billion by 2021 with an staggering CAGR of 29.5% (2016-2021) , translating into an appealing business opportunity for first movers (like AIA Science). With our AIA SGA project, we will increase our revenue to €9.75 million and to create 20 new direct specialized jobs only 3 years after starting commercial activities.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/828176 |
| Start date: | 01-08-2018 |
| End date: | 30-11-2018 |
| Total budget - Public funding: | 71 429,00 Euro - 50 000,00 Euro |
Cordis data
Original description
The 4th industrial revolution is ongoing. The manufacturing and process industry have started transitioning into automated and data exchange operations. Still, the challenge for these players is to handle the massive amounts of data that is produced in large-scale real-time operations –and quite often– needs to be managed under time-pressure. As of now, the process industry has to choose between implementing large-scale analytics systems (using a scale-out or scale-up architecture) to control real-time operations and find metrics for predictions or alternatively, rely on machine learning (ML) technologies to discover meaning in data sets. At AIA Science –a spin-off from Numascale, world leader in high-computing performance or HPC– we have created AIA SGA, the first platform that integrates in a single solution a scale-up graph analytics architecture (based on HPC hardware) and the most advanced ML technology, allowing to automate real-time operations, whilst optimizing production and using anomaly detection to prevent accidents (e.g. the Deepwater Horizon oil spill disaster). Our current prototype (TRL6), combines three main components: The AIA Analytics engine; the AIA ML Suite and Neo4j Graph database software. Our priority market will be the European oil/gas and aluminium industries, having already a solid network of partners within our home market (e.g. Statoil, Aker BP or Norsk Hydro), where most European oil rigs and aluminium plants are concentrated. The market of ML-based large-scale analytics is gaining traction, being expected to be worth €1.1 billion by 2021 with an staggering CAGR of 29.5% (2016-2021) , translating into an appealing business opportunity for first movers (like AIA Science). With our AIA SGA project, we will increase our revenue to €9.75 million and to create 20 new direct specialized jobs only 3 years after starting commercial activities.Status
CLOSEDCall topic
EIC-SMEInst-2018-2020Update Date
27-10-2022
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