Explore the words cloud of the QROWD project. It provides you a very rough idea of what is the project "QROWD" about.
The following table provides information about the project.
Coordinator |
UNIVERSITY OF SOUTHAMPTON
Organization address contact info |
Coordinator Country | United Kingdom [UK] |
Project website | http://qrowd-project.eu/ |
Total cost | 3˙993˙505 € |
EC max contribution | 2˙969˙367 € (74%) |
Programme |
1. H2020-EU.2.1.1. (INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)) |
Code Call | H2020-ICT-2016-1 |
Funding Scheme | IA |
Starting year | 2016 |
Duration (year-month-day) | from 2016-12-01 to 2019-11-30 |
Take a look of project's partnership.
# | ||||
---|---|---|---|---|
1 | UNIVERSITY OF SOUTHAMPTON | UK (SOUTHAMPTON) | coordinator | 651˙504.00 |
2 | INSTITUT FUR ANGEWANDTE INFORMATIK (INFAI) EV | DE (LEIPZIG) | participant | 753˙250.00 |
3 | UNIVERSITA DEGLI STUDI DI TRENTO | IT (TRENTO) | participant | 376˙625.00 |
4 | ATOS SPAIN SA | ES (MADRID) | participant | 336˙437.00 |
5 | COMUNE DI TRENTO | IT (TRENTO) | participant | 249˙375.00 |
6 | INMARK EUROPA SA | ES (MADRID) | participant | 219˙100.00 |
7 | TOMTOM LOCATION TECHNOLOGY GERMANY GMBH | DE (BERLIN) | participant | 201˙346.00 |
8 | TOMTOM DEVELOPMENT GERMANY GMBH | DE (LEIPZIG) | participant | 181˙728.00 |
9 | AI4BD GMBH | CH (ZURICH) | participant | 0.00 |
Big Data integration in European cities is of utmost importance for municipalities and companies to offer effective information services, enable efficient data-driven transportation and mobility, reduce CO2 emissions, assess the efficiency of infrastructure, as well as enhance the quality of life of citizens. At present this integration is substantially limited due to the following factors: 1) Urban Big Data is locked in isolated industrial and public sectors, and 2) The actual Big Data integration is an extremely hard technical problem due to the heterogeneity of data sources, variety of formats, sizes, quality as well as update rates, such that the integration requires significant human intervention.
QROWD addresses these challenges by offering methods to perform cross-sectoral streaming Big Data integration including geographic, transport, meteorological, cross domain and news data, while capitalizing on human feedback channels. The main objectives of QROWD are: (1) Facilitating cross-sectoral Big Data stream integration for urban mobility including real-time data on individual and public transportation combined with further available sources, such as weather conditions and infrastructure information to create a comprehensive overview of the city traffic; (2) Supporting participation and feedback of various stakeholder groups to foster data-driven innovation in cities; and (3) Building a platform providing hybrid computational methods relying on efficient algorithms complemented with human computation and feedback.
The main outcomes of QROWD are: (1) Two data value chains in the sectors of urban mobility and public transportation using a mix of large scale heterogeneous multilingual datasets; and (2) Cross-sectoral and cross-lingual technology, including algorithms and tools covering all phases of the cross-sectoral Big Data Value Chain building on W3C standards and capitalizing on a flexible and efficient combination of human and machine-based computation.
Benchmarking registry, reporting, and crowdsourcing monitoring tools | Other | 2020-03-24 10:50:12 |
Hackathon | Other | 2020-03-24 10:49:56 |
Final TomTom pilot | Documents, reports | 2020-03-24 10:50:06 |
Dynamic data integration, storage and access | Documents, reports | 2020-03-24 10:49:50 |
Integrated processing of data-in-motion and data-at-rest | Demonstrators, pilots, prototypes | 2020-03-24 10:49:45 |
Methods for task and time management | Documents, reports | 2020-03-24 10:49:22 |
Road information services | Demonstrators, pilots, prototypes | 2020-03-24 10:50:15 |
Business plans | Documents, reports | 2020-03-24 10:50:13 |
Linked Data generation framework | Demonstrators, pilots, prototypes | 2020-03-24 10:49:48 |
Link discovery and data fusion algorithms | Open Research Data Pilot | 2020-03-24 10:49:48 |
Data acquisition framework | Demonstrators, pilots, prototypes | 2020-03-24 10:49:24 |
Final Trento pilot | Documents, reports | 2020-03-24 10:50:28 |
iLog | Demonstrators, pilots, prototypes | 2020-03-24 10:50:12 |
Crowdsourcing vocabulary and licensing | Open Research Data Pilot | 2020-03-24 10:48:58 |
Spatio-temporal analytics | Demonstrators, pilots, prototypes | 2020-03-24 10:49:48 |
QROWD platform | Demonstrators, pilots, prototypes | 2020-03-24 10:49:49 |
Outreach report v2 | Documents, reports | 2020-03-24 10:50:20 |
Public endpoints and deployment | Open Research Data Pilot | 2020-03-24 10:49:24 |
Crowdsourced multilingual data harvesting and extraction framework | Demonstrators, pilots, prototypes | 2020-03-24 10:49:22 |
Ideas competition | Other | 2020-02-13 13:44:42 |
Data catalog | Other | 2020-02-13 13:44:41 |
Participatory framework | Documents, reports | 2020-02-13 13:44:40 |
Data management plan | Open Research Data Pilot | 2020-02-13 13:44:42 |
Data storage and access component | Demonstrators, pilots, prototypes | 2019-11-06 11:38:08 |
Urban mobility dashboard | Demonstrators, pilots, prototypes | 2019-11-06 11:38:08 |
Outreach report v1 | Documents, reports | 2019-11-06 11:38:08 |
Business case requirements and design | Documents, reports | 2019-11-06 11:38:08 |
Datasets | Other | 2019-11-06 11:38:08 |
Data quality assessment services | Open Research Data Pilot | 2019-11-06 11:38:08 |
Real-time inductive analysis | Demonstrators, pilots, prototypes | 2019-11-06 11:38:08 |
Requirements and architecture | Documents, reports | 2019-11-06 11:38:08 |
Online presence and brand guidelines | Websites, patent fillings, videos etc. | 2019-11-06 11:38:08 |
Exploitation strategy | Documents, reports | 2019-11-06 11:38:08 |
Crowdsourcing services | Demonstrators, pilots, prototypes | 2019-11-06 11:38:08 |
Take a look to the deliverables list in detail: detailed list of QROWD deliverables.
year | authors and title | journal | last update |
---|---|---|---|
2018 |
Maddalena, Eddy; Ibáñez, Luis-Daniel; Simperl, Elena; Zeni, Mattia; Bignotti, Enrico; Giunchiglia, Fausto; Stadler, Claus; Westphal, Patrick; Garcia, Luis P.F.; Lehmann, Jens QROWD: Because Big Data Integration is Humanly Possible published pages: , ISSN: , DOI: 10.5281/zenodo.3568169 |
2020-01-28 | |
2018 |
Westphal, Patrick; Fernández, Javier D.; Kirrane, Sabrina; Lehmann, Jens SPIRIT: A Semantic Transparency and Compliance Stack published pages: , ISSN: , DOI: 10.5281/zenodo.3567866 |
2020-01-28 | |
2018 |
Vougiouklis, Pavlos; Maddalena, Eddy; Hare, Jonathon; Simperl, Elena How Biased Is Your NLG Evaluation? published pages: , ISSN: , DOI: 10.5281/zenodo.3568173 |
Joint Proceedings SAD 2018 and CrowdBias 2018 | 2020-01-28 |
2018 |
Maddalena, Eddy; Ibáñez, Luis-Daniel; Simperl, Elena On the mapping of Points of Interest through StreetView Imagery and paid crowdsourcing published pages: , ISSN: , DOI: 10.5281/zenodo.3568200 |
2020-01-28 | |
2019 |
Patrick Westphal, Lorenz Bühmann, Simon Bin, Hajira Jabeen, Jens Lehmann SML-Bench – A benchmarking framework for structured machine learning published pages: 231-245, ISSN: 1570-0844, DOI: 10.3233/sw-180308 |
Semantic Web 10/2 | 2020-01-28 |
2019 |
Oluwaseyi Feyisetan, Elena Simperl Beyond Monetary Incentives published pages: 1-31, ISSN: 2469-7818, DOI: 10.1145/3321700 |
ACM Transactions on Social Computing 2/2 | 2020-01-28 |
2019 |
Stadler, Claus; Sejdiu, Gezim; Graux, Damian; Lehmann, Jens Querying Large-scale RDF Datasets Using the SANSA Framework published pages: , ISSN: , DOI: 10.5281/zenodo.3567886 |
Proceedings of the ISWC 2019 Satellite Tracks (Posters & Demonstrations, Industry, and Outrageous Ideas) | 2020-01-28 |
2018 |
F. Giunchiglia, E. Bignotti, and M. Zeni Combining Crowdsourcing and Crowdsensing to Infer the Spatial Context published pages: , ISSN: , DOI: |
International Workshop on Context-Awareness for Multi-Device Pervasive and Mobile Computing | 2019-11-06 |
2018 |
F. Giunchiglia, M. Zeni, E. Bignotti Personal Context Recognition via Reliable Human-Machine Collaboration published pages: , ISSN: , DOI: |
Workshop on Information Quality and Quality of Service for Pervasive Computing | 2019-11-06 |
2018 |
Fausto Giunchiglia, Enrico Bignotti, Mattia Zeni Human-Like Context Sensing for Robot Surveillance published pages: 129-148, ISSN: 1793-7108, DOI: 10.1142/s1793351x1840007x |
International Journal of Semantic Computing 12/01 | 2019-11-06 |
2018 |
F. Giunchiglia, E. Bignotti, M. Zeni, and Wanyi Zhang Assessing Consistency of in the wild Annotations published pages: , ISSN: , DOI: |
2nd International Workshop on Annotation of useR Data for UbiquitOUs Systems | 2019-11-06 |
2018 |
Patrick Westphal
Lorenz Bühmann
Simon Bin
Hajira Jabeen
Jens Lehmann SML-Bench -- A Benchmarking Framework for Structured Machine Learning published pages: , ISSN: 1570-0844, DOI: |
Semantic Web – Interoperability, Usability, Applicability | 2019-11-06 |
2017 |
Fausto Giunchiglia, Mattia Zeni, Elisa Gobbi, Enrico Bignotti, Ivano Bison Mobile Social Media and Academic Performance published pages: 3-13, ISSN: , DOI: 10.1007/978-3-319-67256-4_1 |
2019-11-06 | |
2018 |
Claus Stadler, Muhammad Saleem, Axel-Cyrille Ngonga Ngomo, Jens Lehmann Efficiently Pinpointing SPARQL Query Containments published pages: 210-224, ISSN: , DOI: 10.1007/978-3-319-91662-0_16 |
2019-11-06 |
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The information about "QROWD" are provided by the European Opendata Portal: CORDIS opendata.