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

Projektinformationen

PROTEUS

ID Finanzhilfevereinbarung: 687691

DOI

10.3030/687691

Projekt abgeschlossen

EK-Unterschriftsdatum 4 November 2015

Startdatum 1 Dezember 2015

Enddatum 30 November 2018

Finanziert unter

• INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)

Gesamtkosten

€ 3 156 525,00

EU-Beitrag

€ 3 156 525,00

Koordiniert durch

BOURNEMOUTH UNIVERSITY
United Kingdom

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Demonstrators, pilots, prototypes (17)

Final demonstrator

Final demonstrator together with the final report including the evaluation of the whole technology developed during the project in the Hot Strip Mill process in the ArcelorMittal steelmaking factory.

Hybrid computation tested system

Tested system implementation of hybrid computation for Apache Flink

Second prototype (V2)

Second version of the above, for the second prototype

Optimizer Prototype

Prototype of a domain specific optimizer for the declarative language and Apache Flink

Scalable online machine learning algorithms for streaming

This deliverable will introduce Version 1 of SOLMA that encompasses new scalable online machine learning algorithms.

Optimizer finished implementation

Finished implementation of a domain specific optimizer for the declarative language and Apache Flink.

Updateable-state management prototype implementation

Implemented system for updateable state for Apache Flink

Third prototype (V3)

Third version of the above, for the third prototype

Software implementation and integration with Apache Flink

This deliverable includes the implementation of the 3 layers of the proposed technical solution. Data Collector, and Incremental Analytics Engine layers will be implemented within the core of Apache Flink technology. The Visualization layer will be implemented as client-side library

Basic scalable streaming algorithms

This deliverable is in the form of software (joint with publications) will present Version 0 of the library covering a set of basic scalable streaming algorithms produced in Task 4.2.

Scalable drift and anomaly detection

This deliverable will result in Version 2 of SOLMA covering new scalable drift and anomaly detection algorithms.

Hybrid computation prototype implementation

Prototypical implementation of hybrid computation for Apache Flink covering basic workflows

First prototype (V1)

The first version of the evolving prototype in the validation scenario. An associated evolving document will provide, for each prototype execution, the objectives definition, KPIs involved and their evaluation after the prototype execution phase.

Declarative language tested implementation

Tested implementation of a declarative language for (online) machine learning

Declarative language finished implementation

Finished implementation of a declarative language for (online) machine learning

Scalable Online algorithms in Flink

This deliverable will release the final implementation in Flink of the streaming algorithms produced earlier through D4.2-D4.4.

Declarative language prototype implementation

Implementation of a basic declarative language prototype

Documents, reports (15)

Report on scientific dissemination activities – V1

Details for scientific dissemination activities and materials along with the time line and success indicators. It includes a record of activities related to scientific dissemination that have been undertaken during the first half of the project, and those planned for the second period.

Scenario details and objectives description

This document details the Hot Strip Mill process in terms of sensor data characteristics and data workflow. It also describes the scenario objectives from the end-user perspective.

Report on community engagement and technology transfer activities – V2

The final version of the deliverable compiles a record of all the activities related to community engagement and technology transfer developed in the course of the project

Scenario development and KPI definition for the PROTEUS solution

A report that presents the review of benchmarks, the typical scenarios used to define the parameters of the PROTEUS solution and requirements, benchmarks and KPIs

PROTEUS evaluation and impact assessment

A report which details the gains associated with the PROTEUS solution, using quantitative information, and which identifies areas for further improvement and investment

Guidelines for interacting and visualization information in Big Data environments

This document presents the results of the research in new ways of presenting and working with large amount of data and stream data

Visualization requirements for massive online machine learning strategies

This deliverable defines functional and non-functional requirements for the visualization system regarding online machine learning strategies

Report on project communication and engagement activities – V2

The final report of communication and engagement activities, compiling a list of all activities developed for communication with other relevant initiatives in the course of the project.

Report on scientific dissemination activities – V2 [

Final report of scientific dissemination activities. The final version compiles a record of all activities related to scientific dissemination developed in the course of the project.

Catalogue of scientific and technical requirements

This document describes the catalogue of scientific and technical challenges/requirements derived from the industrial scenario needs.

Report on community engagement and technology transfer activities – V1

Details for the community engagement and technology transfer strategy for the project. The intermediate report includes a record of activities related to community creation and engagement, and technology transfer developed in the course of the first half of the project, and those planned for the second half

Declarative language syntax definition

Syntax definition for a declarative language based on machine learning requirements

Architecture design for supporting incremental visual methods

This deliverable defines the technical design of the 3-layer based architecture for implementing the visualization system

Report on project communication and engagement activities – V1

Details for communication and engagement activities and materials along with the time line and success indicators. It includes a record of communication activities that have been undertaken during the first half of the project, and those planned for the second period.

Investigative overview of targeted techniques and algorithms

The state of the art of scalable streaming algorithms for distributed environments, non-scalable streaming algorithms, and selected prominent non-streaming and non-scalable algorithms that can be approximated by an online version.

Websites, patent fillings, videos etc. (2)

PROTEUS factsheet leaflet

The PROTEUS factsheet will be an early dissemination leaflet for dissmeination and communication purposes, including the most relevant information of the project in a nutshell, and will be available from the very begining as an initial public brochure.

PROTEUS project website

PROTEUS project public website, to be active and regularly updated during the whole project.

Veröffentlichungen

Conference proceedings (12)

Efficient Migration of Very Large Distributed State for Scalable Streaming Processing

Autoren: Bonaventura Del Monte
Veröffentlicht in: Proceedings of the VLDB 2017 PhD Workshop, Ausgabe 28 August 2017, 2017
Herausgeber: N/A

Non-dominated solutions visualization in multiobjective optimization: application to assembly line balancing

Autoren: Krzysztof Trawinski, Manuel Chica, David P. Pancho, Sergio Damas, and Oscar Cordón
Veröffentlicht in: Proceeding of the MIC and MAEB 2017 Conferences, Ausgabe June 2017, 2017, Seite(n) 963-972, ISBN 978-84-697-4275-1
Herausgeber: Universitat Pompeu Fabra

Scotty: Efficient Window Aggregation for Out-of-Order Stream Processing

Autoren: Jonas Traub, Philipp Marian Grulich, Alejandro Rodriguez Cuellar, Sebastian Bress, Asterios Katsifodimos, Tilmann Rabl, Volker Markl
Veröffentlicht in: 2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018, Seite(n) 1300-1303, ISBN 978-1-5386-5520-7
Herausgeber: IEEE
DOI: 10.1109/ICDE.2018.00135

Scalable online learning for flink - SOLMA library

Autoren: W. Jamil, N-C. Duong, W. Wang, C. Mansouri, S. Mohamad, A. Bouchachia
Veröffentlicht in: Proceedings of the 12th European Conference on Software Architecture Companion Proceedings - ECSA '18, 2018, Seite(n) 1-4, ISBN 9781-450364836
Herausgeber: ACM Press
DOI: 10.1145/3241403.3241438

Benchmarking Distributed Stream Data Processing Systems

Autoren: Jeyhun Karimov, Tilmann Rabl, Asterios Katsifodimos, Roman Samarev, Henri Heiskanen, Volker Markl
Veröffentlicht in: 2018 IEEE 34th International Conference on Data Engineering (ICDE), 2018, Seite(n) 1507-1518, ISBN 978-1-5386-5520-7
Herausgeber: IEEE
DOI: 10.1109/ICDE.2018.00169

Aggregation Algorithm Vs. Average for Time Series Prediction

Autoren: Bouchachia, Abdelhamid; Kalnishkan, Y; Jamil, W.
Veröffentlicht in: ECML/PKDD 2016 Workshop on Large-scale Learning from Data Streams in Evolving Environments (STREAMEVOLV-2016), Ausgabe 1, 2016, Seite(n) 69-82
Herausgeber: N/A

Bridging the gap: towards optimization across linear and relational algebra

Autoren: Andreas Kunft, Alexander Alexandrov, Asterios Katsifodimos, Volker Markl
Veröffentlicht in: BeyondMR '16 Proceedings of the 3rd ACM SIGMOD Workshop on Algorithms and Systems for MapReduce and Beyond, Ausgabe BeyondMR '16 26-06-2016, 2016, ISBN 978-1-4503-4311-4
Herausgeber: ACM
DOI: 10.1145/2926534.2926540

Emma in Action: Declarative Dataflows for Scalable Data Analysis

Autoren: Alexander Alexandrov , Andreas Salzmann , Georgi Krastev , Asterios Katsifodimos , Volker Markl
Veröffentlicht in: ACM SIGMOD '16 Proceedings of the 2016 SIGMOD International Conference on Management of Data, Ausgabe Sigmod16, 26-06-2016, 2016, Seite(n) 2073-2076, ISBN 978-1-4503-3531-7
Herausgeber: ACM
DOI: 10.1145/2882903.2899396

Implicit Parallelism through Deep Language Embedding

Autoren: Alexander Alexandrov, Asterios Katsifodimos, Georgi Krastev, Volker Markl
Veröffentlicht in: ACM SIGMOD Record, Ausgabe Volume 45, Number 1, March 2016, 2016, Seite(n) 51-58, ISSN 0163-5808
Herausgeber: ACM
DOI: 10.1145/2949741.2949754

An Incremental Approach for Real-Time Big Data Visual Analytics

Autoren: Ignacio Garcia, Ruben Casado, Abdelhamid Bouchachia
Veröffentlicht in: 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), 2016, Seite(n) 177-182, ISBN 978-1-5090-3946-3
Herausgeber: IEEE
DOI: 10.1109/W-FiCloud.2016.46

A non-parametric hierarchical clustering model

Autoren: Saad Mohamad, Abdelhamid Bouchachia, Moamar Sayed-Mouchaweh
Veröffentlicht in: 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), Ausgabe STREAMEVOLV-2016, 23 September 2016, 2015, Seite(n) 1-7, ISBN 978-1-4673-6698-4
Herausgeber: IEEE
DOI: 10.1109/EAIS.2015.7368803

Active Learning for Data Streams under Concept Drift and concept evolution

Autoren: Saad Mohamad, Moamar Sayed-Mouchaweh and Abdelhamid Bouchachia
Veröffentlicht in: ECML/PKDD 2016 Workshop on Large-scale Learning from Data Streams in Evolving Environments, Ausgabe STREAMEVOLV-2016, 23 September 2016, 2016, Seite(n) 51-68
Herausgeber: -

Peer reviewed articles (7)

LIBIRWLS: A parallel IRWLS library for full and budgeted SVMs

Autoren: Roberto Díaz-Morales, Ángel Navia-Vázquez
Veröffentlicht in: Knowledge-Based Systems, Ausgabe 136, 2017, Seite(n) 183-186, ISSN 0950-7051
Herausgeber: Elsevier BV
DOI: 10.1016/j.knosys.2017.09.007

Batch-based active learning: Application to social media data for crisis management

Autoren: Daniela Pohl, Abdelhamid Bouchachia, Hermann Hellwagner
Veröffentlicht in: Expert Systems with Applications, Ausgabe 93, 2018, Seite(n) 232-244, ISSN 0957-4174
Herausgeber: Pergamon Press Ltd.
DOI: 10.1016/j.eswa.2017.10.026

Active learning for classifying data streams with unknown number of classes

Autoren: Saad Mohamad, Moamar Sayed-Mouchaweh, Abdelhamid Bouchachia
Veröffentlicht in: Neural Networks, Ausgabe 98, 2018, Seite(n) 1-15, ISSN 0893-6080
Herausgeber: Pergamon Press Ltd.
DOI: 10.1016/j.neunet.2017.10.004

MSAFIS: an evolving fuzzy inference system

Autoren: José de Jesús Rubio, Abdelhamid Bouchachia
Veröffentlicht in: Soft Computing, Ausgabe 21/9, 2017, Seite(n) 2357-2366, ISSN 1432-7643
Herausgeber: Springer Verlag
DOI: 10.1007/s00500-015-1946-4

Blockjoin

Autoren: Andreas Kunft, Asterios Katsifodimos, Sebastian Schelter, Tilmann Rabl, Volker Markl
Veröffentlicht in: Proceedings of the VLDB Endowment, Ausgabe 10/13, 2017, Seite(n) 2061-2072, ISSN 2150-8097
Herausgeber: ACM
DOI: 10.14778/3151106.3151110

Improving the efficiency of IRWLS SVMs using parallel Cholesky factorization

Autoren: Díaz Morales, R. , & Navia Vázquez, Á
Veröffentlicht in: Pattern Recognition Letters, Ausgabe Volume 84, 1 December 2016, 2016, Seite(n) 91-98, ISSN 0167-8655
Herausgeber: Elsevier BV
DOI: 10.1016/j.patrec.2016.08.015

A Bi-Criteria Active Learning Algorithm for Dynamic Data Streams

Autoren: Mohamad, S., Bouchachia, A. and Sayed-Mouchaweh, M.
Veröffentlicht in: IEEE Transactions on Neural Networks and Learning Systems, Ausgabe N/A (early access), 2016, Seite(n) 1-13, ISSN 2162-2388
Herausgeber: IEEE
DOI: 10.1109/TNNLS.2016.2614393

Book chapters (2)

Model Selection in Online Learning for Times Series Forecasting

Autoren: Waqas Jamil, Abdelhamid Bouchachia
Veröffentlicht in: Advances in Computational Intelligence Systems - Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK, Ausgabe 840, 2019, Seite(n) 83-95, ISBN 978-3-319-97981-6
Herausgeber: Springer International Publishing
DOI: 10.1007/978-3-319-97982-3_7

Fuzzy Classifiers

Autoren: Abdelhamid Bouchachia
Veröffentlicht in: Handbook on Computational Intelligence, Ausgabe May 2016, 2016, Seite(n) 185-207, ISBN 978-981-4675-00-0
Herausgeber: WORLD SCIENTIFIC
DOI: 10.1142/9789814675017_0005

Monographic books (2)

Advances in Computational Intelligence Systems - Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK

Autoren: Ahmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
Veröffentlicht in: Advances in Intelligent Systems and Computing, 2019, ISBN 978-3-319-97982-3
Herausgeber: Springer
DOI: 10.1007/978-3-319-97982-3

ECML/PKDD 2017 Workshop on IoT Large Scale Learning from Data Streams

Autoren: M.S. Mouchaweh, A. Bifet, A. Bouchachia, J. Gama, R. Ribeiro
Veröffentlicht in: 2017
Herausgeber: CEUR-WS.org

Other (1)

Apache Flink: Stream and Batch Processing in a Single Engine

Autoren: Paris Carbone, Stephan Ewen, Seif Haridi, Asterios Katsifodimos, Volker Markl, Kostas Tzoumas
Veröffentlicht in: Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, Ausgabe December 2015 Vol. 38 No. 4, Ausgabe on Next-Generation Stream Processing Systems, 2015, Seite(n) 28-38
Herausgeber: IEEE