Project description
A new machine learning platform for data management
The process manufacturing industry relies increasingly on automated data exchange operations. However, managing the massive amounts of data produced in large-scale real-time operations represents a significant challenge. Therefore, the industry has two main options: implementing large-scale analytics systems or turning to machine learning (ML) technologies. The EU-funded AIASGA project proposes the first-of-its-kind platform that integrates a high-performance computing hardware-based scale-up graph analytics architecture with the most advanced ML technology. The platform allows automated real-time operations, optimises production and uses anomaly detection to prevent accidents.
Objective
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences chemical sciences inorganic chemistry post-transition metals
- natural sciences computer and information sciences data science data exchange
- natural sciences computer and information sciences databases non-relational databases
- natural sciences computer and information sciences artificial intelligence machine learning
- natural sciences computer and information sciences software software applications
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.2.3. - INDUSTRIAL LEADERSHIP - Innovation In SMEs
MAIN PROGRAMME
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H2020-EU.3. - PRIORITY 'Societal challenges
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H2020-EU.2.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
SME-1 - SME instrument phase 1
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-EIC-SMEInst-2018-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
7026 Trondheim
Norway
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.