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NOESIS SIGNED

NOvel Decision Support tool for Evaluating Strategic Big Data investments in Transport and Intelligent Mobility Services

Total Cost €

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EC-Contrib. €

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Partnership

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Project "NOESIS" data sheet

The following table provides information about the project.

Coordinator
ORTELIO LTD 

Organization address
address: PUMA WAY COVENTRY UNIVERSITY TECHNOLOGY PARK
city: COVENTRY
postcode: CV1 2TT
website: n.a.

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 https://noesis-project.eu
 Total cost 1˙197˙831 €
 EC max contribution 1˙197˙831 € (100%)
 Programme 1. H2020-EU.3.4. (SOCIETAL CHALLENGES - Smart, Green And Integrated Transport)
 Code Call H2020-MG-2017-SingleStage-INEA
 Funding Scheme CSA
 Starting year 2017
 Duration (year-month-day) from 2017-11-01   to  2019-10-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ORTELIO LTD UK (COVENTRY) coordinator 314˙875.00
2    TECHNISCHE UNIVERSITAET MUENCHEN DE (MUENCHEN) participant 187˙375.00
3    UNIVERSIDAD POLITECNICA DE MADRID ES (MADRID) participant 135˙625.00
4    KUNGLIGA TEKNISKA HOEGSKOLAN SE (STOCKHOLM) participant 132˙562.00
5    COVENTRY UNIVERSITY ENTERPRISES LIMITED UK (COVENTRY) participant 132˙375.00
6    Univerzitet u Beogradu - Saobracajni fakultet RS (Belgrade) participant 125˙031.00
7    BAYERISCHE AKADEMIE DER WISSENSCHAFTEN DE (MUENCHEN) participant 93˙706.00
8    MACOMI BV NL (DEN HOORN) participant 76˙281.00

Map

 Project objective

NOESIS project will identify the critical factors/features which lead to successful implementation of Big Data technologies and services in the field of transport and logistics with significant value generation from a socioeconomic viewpoint. This will be achieved through the examination of areas and contexts throughout Europe, in which ICT investments and exploitation of data should be implemented. The impact of Big Data will be evaluated in a series of transportation use cases (Big Data in Transport Library) by developing and applying a ‘Learning framework’ and a Value Capture mechanism which will estimate the expected benefits and costs.

 Deliverables

List of deliverables.
Policy briefs Documents, reports 2020-04-24 04:31:03
Data Benefit Analysis and Impact Assessment Methodologies (IAM) for appraising big data solution in transport Documents, reports 2020-04-24 04:31:03
Exploitation Plan Documents, reports 2020-04-24 04:31:03
Technological and policy roadmaps Documents, reports 2020-04-24 04:31:03
Development and validation of the NOESIS Decision Support tool (DST) Documents, reports 2020-04-24 04:31:03
Suitability of business and organizational models for the successful implementation of big data in transport solutions Documents, reports 2020-04-24 04:31:03
Dissemination , communication and exploitation plan Documents, reports 2020-04-24 04:31:03
Handbook on Key Lessons Learnt and Transferable Practices Documents, reports 2020-04-24 04:31:03
Big Data implementation context in transport Documents, reports 2020-04-24 04:31:02
Data governance and institutional issues Documents, reports 2020-04-24 04:31:03
Big Data in Transport Library Documents, reports 2020-04-24 04:31:02
Learning Framework methodology and architecture Documents, reports 2020-04-24 04:31:03
Summary to Practitioners on Laws, Regulations, and Directives on Data Privacy, Security and Openness Documents, reports 2020-04-24 04:31:03
Big Data and emerging transportation challenges Documents, reports 2020-04-24 04:31:03

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

 Publications

year authors and title journal last update
List of publications.
2019 Xiang Fei, Nazaraf Shah, Nandor Verba, Kuo-Ming Chao, Victor Sanchez-Anguix, Jacek Lewandowski, Anne James, Zahid Usman
CPS data streams analytics based on machine learning for Cloud and Fog Computing: A survey
published pages: 435-450, ISSN: 0167-739X, DOI: 10.1016/j.future.2018.06.042
Future Generation Computer Systems 90 2020-04-24
2018 Slađana Janković, Snežana Mladenović, Dušan Mladenović, Slavko Vesković, Draženko Glavić
Schema on read modeling approach as a basis of big data analytics integration in EIS
published pages: 1180-1201, ISSN: 1751-7575, DOI: 10.1080/17517575.2018.1462404
Enterprise Information Systems 12/8-9 2020-04-24

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

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