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DATAMINE4.0 TERMINATED

Advanced Data Modeling and Analysis Applied to next Generation Industry 4.0 settings and the Internet of Things

Total Cost €

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EC-Contrib. €

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Partnership

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Project "DATAMINE4.0" data sheet

The following table provides information about the project.

Coordinator
IRIS TECHNOLOGY SOLUTIONS, SOCIEDAD LIMITADA 

Organization address
address: CALLE VELAZQUEZ, NO 94 PRIMERA PLANTA
city: MADRID
postcode: 28006
website: www.iris.cat

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 Spain [ES]
 Total cost 170˙121 €
 EC max contribution 170˙121 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-SE
 Starting year 2018
 Duration (year-month-day) from 2018-01-22   to  2020-01-21

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    IRIS TECHNOLOGY SOLUTIONS, SOCIEDAD LIMITADA ES (MADRID) coordinator 170˙121.00

Map

 Project objective

DATAMINE4.0 is an IF for Career Development for Natasa Sarafijanovic-Djukic, PhD in Computer/Communication Sciences obtained from EPFL, Switzerland, a researcher in statistical modelling originally from Serbia, whereby she will work in IRIS, a Spanish R&D and advanced engineering SME. The project will study the modeling and analysis of data coming from complex industrial and organizational applications, in order to identify hidden causistic relationships in order to calibrate production and organizational control systems. It is highly multidisciplinary combining data science, statistics, artificial intelligence, telecommunications, specific engineering and organizational domains and information technology processing competences. In line with new paradigms such as Industry 4.0 and the Internet of Things. Although machine learning paradigms have been in existence for several decades, their penetration in the industrial and public organization setting has been limited. The goal is to build calibration and optimization models using advanced statistical and machine learning methods and other artificial intelligence inspired algorithms, for applications in which existing methods have been unsatisfactory. This will have a big impact on productivity and repeatability of winning formulas for key industrial applications such as customized production, public sector applications (e.g. optimization of urban mobility), and food processing. This is in line with findings published by the European Union. The developed data mining solutions will greatly enhance the current performance of the selected applications, allowing them a competitive advantage over current methods. The work will be done in an applied R&D environment in line with the objective for Dr. Natasa Sarafijanovic-Djukic’s career continuation. An engineering approach will be ideally completed with 7 formal scientific trainings, including two academic secondments, each of 2 months.

 Publications

year authors and title journal last update
List of publications.
2019 David F. Nettleton, Dimitrios Katsantonis, Argyris Kalaitzidis, Natasa Sarafijanovic-Djukic, Pau Puigdollers, Roberto Confalonieri
Predicting rice blast disease: machine learning versus process-based models
published pages: , ISSN: 1471-2105, DOI: 10.1186/s12859-019-3065-1
BMC Bioinformatics 20/1 2020-01-30

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The information about "DATAMINE4.0" are provided by the European Opendata Portal: CORDIS opendata.

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