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PROTEUS SIGNED

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

Total Cost €

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EC-Contrib. €

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Partnership

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Project "PROTEUS" data sheet

The following table provides information about the project.

Coordinator
BOURNEMOUTH UNIVERSITY 

Organization address
address: FERN BARROW BOURNEMOUTH UNIVERSITY
city: POOLE
postcode: BH12 5BB
website: www.bournemouth.ac.uk

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country United Kingdom [UK]
 Project website http://www.proteus-bigdata.com
 Total cost 3˙156˙525 €
 EC max contribution 3˙156˙525 € (100%)
 Programme 1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT))
 Code Call H2020-ICT-2015
 Funding Scheme RIA
 Starting year 2015
 Duration (year-month-day) from 2015-12-01   to  2018-11-30

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    BOURNEMOUTH UNIVERSITY UK (POOLE) coordinator 627˙774.00
2    DEUTSCHES FORSCHUNGSZENTRUM FUR KUNSTLICHE INTELLIGENZ GMBH DE (KAISERSLAUTERN) participant 1˙035˙750.00
3    TREELOGIC TELEMATICA Y LOGICA RACIONAL PARA LA EMPRESA EUROPEA SL ES (LLANERA ASTURIAS) participant 700˙294.00
4    TRILATERAL RESEARCH LTD UK (LONDON) participant 333˙750.00
5    Lambdoop Solutions S.L. ES (MADRID) participant 243˙330.00
6    ARCELORMITTAL INNOVACION INVESTIGACION E INVERSION SL ES (GOZON) participant 215˙625.00
7    TRILATERAL RESEARCH & CONSULTING LLP UK (LONDON) participant 0.00

Map

 Project 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.

 Deliverables

List of deliverables.
Report on scientific dissemination activities – V2 [ Documents, reports 2020-02-20 17:17:37
PROTEUS factsheet leaflet Websites, patent fillings, videos etc. 2020-02-20 17:17:34
Catalogue of scientific and technical requirements Documents, reports 2020-02-20 17:17:35
Updateable-state management prototype implementation Demonstrators, pilots, prototypes 2020-02-20 17:17:34
Investigative overview of targeted techniques and algorithms Documents, reports 2020-02-20 17:17:34
Report on community engagement and technology transfer activities – V1 Documents, reports 2020-02-20 17:17:34
Guidelines for interacting and visualization information in Big Data environments Documents, reports 2020-02-20 17:17:34
Report on project communication and engagement activities – V1 Documents, reports 2020-02-20 17:17:34
Declarative language syntax definition Documents, reports 2020-02-20 17:17:35
Basic scalable streaming algorithms Demonstrators, pilots, prototypes 2020-02-20 17:17:35
Declarative language prototype implementation Demonstrators, pilots, prototypes 2020-02-20 17:17:34
Report on scientific dissemination activities – V1 Documents, reports 2020-02-20 17:17:34
Hybrid computation prototype implementation Demonstrators, pilots, prototypes 2020-02-20 17:17:34
Hybrid computation tested system Demonstrators, pilots, prototypes 2020-02-20 17:17:35
Visualization requirements for massive online machine learning strategies Documents, reports 2020-02-20 17:17:35
First prototype (V1) Demonstrators, pilots, prototypes 2020-02-20 17:17:34
PROTEUS project website Websites, patent fillings, videos etc. 2020-02-20 17:17:35
Scenario details and objectives description Documents, reports 2020-02-20 17:17:34
Scenario development and KPI definition for the PROTEUS solution Documents, reports 2020-02-20 17:17:35
Scalable Online algorithms in Flink Demonstrators, pilots, prototypes 2020-02-20 17:17:34
Optimizer finished implementation Demonstrators, pilots, prototypes 2020-02-20 17:17:33
Architecture design for supporting incremental visual methods Documents, reports 2020-02-20 17:17:34
Report on community engagement and technology transfer activities – V2 Documents, reports 2020-02-20 17:17:34
Scalable drift and anomaly detection Demonstrators, pilots, prototypes 2020-02-20 17:17:33
Third prototype (V3) Demonstrators, pilots, prototypes 2020-02-20 17:17:33
PROTEUS evaluation and impact assessment Documents, reports 2020-02-20 17:17:34
Declarative language finished implementation Demonstrators, pilots, prototypes 2020-02-20 17:17:33
Declarative language tested implementation Demonstrators, pilots, prototypes 2020-02-20 17:17:33
Final demonstrator Demonstrators, pilots, prototypes 2020-02-20 17:17:33
Optimizer Prototype Demonstrators, pilots, prototypes 2020-02-20 17:17:33
Second prototype (V2) Demonstrators, pilots, prototypes 2020-02-20 17:17:33
Software implementation and integration with Apache Flink Demonstrators, pilots, prototypes 2020-02-20 17:17:34
Scalable online machine learning algorithms for streaming Demonstrators, pilots, prototypes 2020-02-20 17:17:33
Report on project communication and engagement activities – V2 Documents, reports 2020-02-20 17:17:37

Take a look to the deliverables list in detail:  detailed list of PROTEUS deliverables.

 Publications

year authors and title journal last update
List of publications.
2018 Daniela Pohl, Abdelhamid Bouchachia, Hermann Hellwagner
Batch-based active learning: Application to social media data for crisis management
published pages: 232-244, ISSN: 0957-4174, DOI: 10.1016/j.eswa.2017.10.026
Expert Systems with Applications 93 2020-02-20
2015 Paris Carbone, Stephan Ewen, Seif Haridi, Asterios Katsifodimos, Volker Markl, Kostas Tzoumas
Apache Flink: Stream and Batch Processing in a Single Engine
published pages: 28-38, ISSN: , DOI:
Bulletin of the IEEE Computer Society Technical Committee on Data Engineering December 2015 Vol. 38 No. 4, Is 2020-02-20
2017 Bonaventura Del Monte
Efficient Migration of Very Large Distributed State for Scalable Streaming Processing
published pages: , ISSN: , DOI:
Proceedings of the VLDB 2017 PhD Workshop 28 August 2017 2020-02-20
2016 Saad Mohamad, Moamar Sayed-Mouchaweh and Abdelhamid Bouchachia
Active Learning for Data Streams under Concept Drift and concept evolution
published pages: 51-68, ISSN: , DOI:
ECML/PKDD 2016 Workshop on Large-scale Learning from Data Streams in Evolving Environments STREAMEVOLV-2016, 23 September 2020-02-20
2016 Díaz Morales, R. , & Navia Vázquez, Á
Improving the efficiency of IRWLS SVMs using parallel Cholesky factorization
published pages: 91-98, ISSN: 0167-8655, DOI: 10.1016/j.patrec.2016.08.015
Pattern Recognition Letters Volume 84, 1 December 2016 2020-02-20
2017 Roberto Díaz-Morales, Ángel Navia-Vázquez
LIBIRWLS: A parallel IRWLS library for full and budgeted SVMs
published pages: 183-186, ISSN: 0950-7051, DOI: 10.1016/j.knosys.2017.09.007
Knowledge-Based Systems 136 2020-02-20
2016 Bouchachia, Abdelhamid; Kalnishkan, Y; Jamil, W.
Aggregation Algorithm Vs. Average for Time Series Prediction
published pages: 69-82, ISSN: , DOI:
ECML/PKDD 2016 Workshop on Large-scale Learning from Data Streams in Evolving Environments (STREAMEVOLV-2016) 1 2020-02-20
2016 Mohamad, S., Bouchachia, A. and Sayed-Mouchaweh, M.
A Bi-Criteria Active Learning Algorithm for Dynamic Data Streams
published pages: 1-13, ISSN: 2162-2388, DOI: 10.1109/TNNLS.2016.2614393
IEEE Transactions on Neural Networks and Learning Systems N/A (early access) 2020-02-20
2018 Saad Mohamad, Moamar Sayed-Mouchaweh, Abdelhamid Bouchachia
Active learning for classifying data streams with unknown number of classes
published pages: 1-15, ISSN: 0893-6080, DOI: 10.1016/j.neunet.2017.10.004
Neural Networks 98 2020-02-20
2017 José de Jesús Rubio, Abdelhamid Bouchachia
MSAFIS: an evolving fuzzy inference system
published pages: 2357-2366, ISSN: 1432-7643, DOI: 10.1007/s00500-015-1946-4
Soft Computing 21/9 2020-02-20
2017 Andreas Kunft, Asterios Katsifodimos, Sebastian Schelter, Tilmann Rabl, Volker Markl
Blockjoin
published pages: 2061-2072, ISSN: 2150-8097, DOI: 10.14778/3151106.3151110
Proceedings of the VLDB Endowment 10/13 2020-02-20
2017 M.S. Mouchaweh, A. Bifet, A. Bouchachia, J. Gama, R. Ribeiro
ECML/PKDD 2017 Workshop on IoT Large Scale Learning from Data Streams
published pages: , ISSN: , DOI:
2020-02-20

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