Extreme data mining, aggregation and analytics technologies and solutions (RIA) -

Expected Outcome

Proposal results are expected to contribute to the following expected outcomes:

• provide better technologies, tools and solutions for data mining (searching and processing) of large, constantly growing amounts and varieties of data, and/or extremely sparse/dispersed/heterogeneous/multilingual data (stored centrally or in distributed/decentralized systems), in particular IoT, industrial, business, administrative, environmental, scientific or societal data.

Scope

The actions under this topic are expected to provide ground-breaking advances in the performance, speed and/or accuracy as well as usefulness of data discovery, collection, mining, filtering and processing in view of coping with “extreme data”: (defined as data that exhibits one or more of the following characteristics, to an extent that makes current technologies fail: increasing volume, speed, variety; complexity/diversity/multilinguality of data; the dispersed data sources; sparse/missing/insufficient data/extreme variations in values). The technologies and solutions are expected to discover and distil meaningful, reliable and useful data from heterogeneous and dispersed/scarce sources and deliver it to the requesting application/user with minimal delay and in the appropriate format. In particular, the advances should enable the development of trustworthy, accurate, green and fair AI systems where quality of data is as important as quantity and/or support industrial distributed decision-making tasks at appropriate level in the computing continuum (edge/fog/cloud). Insofar the results are intended for human use, the design of these tools should take into account the relevant human aspects and interactions with users.

The actions should address the integration of relevant technologies (e.g. big data, AI, IoT, HPC, edge/fog/cloud computing, language technologies, cybersecurity, telecommunications, autonomous systems etc.) as a means towards achieving the goals, and foster links to the respective research, industrial and user/innovator communities (e.g. AI4EU, digital innovation hubs). The use of European data sources (such as Copernicus, Galileo/EGNOS for satellite data) is encouraged in the use cases, where appropriate.

In this topic the integration of the gender dimension (sex and gender analysis) in research and innovation content is not a mandatory requirement.

Activities are expected to start at TRL 3 and achieve TRL 5 by the end of the project – see General Annex B.

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