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Scalable online machine learning for predictive analytics and real-time interactive visualization

Objective

PROTEUS mission is to investigate and develop ready-to-use scalable online machine learning algorithms and interactive visualization techniques for real-time predictive analytics to deal with extremely large data sets and data streams. The developed algorithms and techniques will form a library to be integrated into an enhanced version of Apache Flink, the EU Big Data platform. PROTEUS will contribute to the EU Big Data area by addressing fundamental challenges related to the scalability and responsiveness of analytics capabilities. The requirements are defined by a steelmaking industrial use case. The techniques developed in PROTEUS are however, general, flexible and portable to all data stream-based domains. In particular, the project will go beyond the current state-of-art technology by making the following specific original contributions:
i) Real-time scalable machine learning for massive, high-velocity and complex data streams analytics;
ii) Real-time hybrid computation, batch data and data streams;
iii) Real-time interactive visual analytics for Big Data;
iv) Enhancement of Apache Flink, the EU Big Data platform; and
v) Real-world industrial validation of the technology developed
The PROTEUS impact is manifold: i) strategic, by reducing the gap and dependency from the US technology, empowering the EU Big Data industry through the enrichment of the EU platform Apache Flink; ii) economic, by fostering the development of new skills and new job positions and opportunities towards economic growth; iii) industrial, by considering real-world requirements from industry and by validating the outcome on an operational setting, and iv) scientific, by developing original hybrid and streaming analytic architectures that enable scalable online machine learning strategies and advanced interactive visualisation techniques that are applicable for general data streams in other domains.

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Keywords

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Programme(s)

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Topic(s)

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Funding Scheme

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RIA - Research and Innovation action

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Call for proposal

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(opens in new window) H2020-ICT-2015

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Coordinator

BOURNEMOUTH UNIVERSITY
Net EU contribution

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.

€ 627 774,73
Address
FERN BARROW BOURNEMOUTH UNIVERSITY
BH12 5BB POOLE
United Kingdom

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Region
South West (England) Dorset and Somerset Bournemouth
Activity type
Higher or Secondary Education Establishments
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Total cost

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.

€ 627 774,73

Participants (5)

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