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Data harvesting, curation and fusion model to support public service recommendations for e-governments

Gayane Sedrakyan (UGent) , Laurens De Vocht (UGent) , J. Alonso, M. Escalante, L. Orue-Echevarria and Erik Mannens (UGent)
(2018) PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT. In Modelsward p.691-698
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Abstract
This work reports on early results from CITADEL project that aims at creating an ecosystem of best practices, tools, and recommendations to transform Public Administrations with more efficient, inclusive and citizen-centric services. The goal of the recommendations is to support Governments to find out why citizens stop using public services, and use this information to re-adjust provision to bring these citizens back in. Furthermore, it will help identifying why citizens are not using a given public service (due to affordability, accessibility, lack of knowledge, embarrassment, lack of interest, etc.) and, where appropriate, use this information to make public services more attractive, so they start using the services. While recommender systems can enhance experiences by providing targeted information, the entry barriers in terms of data acquisition are very high, often limiting recommender solutions to closed systems of user/context models. The main focus of this work is to provide an architectural model that allows harvesting data from various sources, curating datasets that originate from a multitude of formats and fusing them into semantically enhanced data that contain key performance indicators for the utility of e-Government services. The output can be further processed by analytics and/or recommender engines to suggest public service improvement needs.
Keywords
Architectural Model, Recommendation Generation, Public Administration, Public Services, Data Harvesting, Data Curation, Data Fusion, Linked Data, E-Government

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Citation

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MLA
Sedrakyan, Gayane, et al. “Data Harvesting, Curation and Fusion Model to Support Public Service Recommendations for e-Governments.” PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT, 2018, pp. 691–98, doi:10.5220/0006728206910698.
APA
Sedrakyan, G., De Vocht, L., Alonso, J., Escalante, M., Orue-Echevarria, L., & Mannens, E. (2018). Data harvesting, curation and fusion model to support public service recommendations for e-governments. PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT, 691–698. https://doi.org/10.5220/0006728206910698
Chicago author-date
Sedrakyan, Gayane, Laurens De Vocht, J. Alonso, M. Escalante, L. Orue-Echevarria, and Erik Mannens. 2018. “Data Harvesting, Curation and Fusion Model to Support Public Service Recommendations for e-Governments.” In PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT, 691–98. https://doi.org/10.5220/0006728206910698.
Chicago author-date (all authors)
Sedrakyan, Gayane, Laurens De Vocht, J. Alonso, M. Escalante, L. Orue-Echevarria, and Erik Mannens. 2018. “Data Harvesting, Curation and Fusion Model to Support Public Service Recommendations for e-Governments.” In PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT, 691–698. doi:10.5220/0006728206910698.
Vancouver
1.
Sedrakyan G, De Vocht L, Alonso J, Escalante M, Orue-Echevarria L, Mannens E. Data harvesting, curation and fusion model to support public service recommendations for e-governments. In: PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT. 2018. p. 691–8.
IEEE
[1]
G. Sedrakyan, L. De Vocht, J. Alonso, M. Escalante, L. Orue-Echevarria, and E. Mannens, “Data harvesting, curation and fusion model to support public service recommendations for e-governments,” in PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT, Madeira, Portugal, 2018, pp. 691–698.
@inproceedings{8546259,
  abstract     = {{This work reports on early results from CITADEL project that aims at creating an ecosystem of best practices, tools, and recommendations to transform Public Administrations with more efficient, inclusive and citizen-centric services. The goal of the recommendations is to support Governments to find out why citizens stop using public services, and use this information to re-adjust provision to bring these citizens back in. Furthermore, it will help identifying why citizens are not using a given public service (due to affordability, accessibility, lack of knowledge, embarrassment, lack of interest, etc.) and, where appropriate, use this information to make public services more attractive, so they start using the services. While recommender systems can enhance experiences by providing targeted information, the entry barriers in terms of data acquisition are very high, often limiting recommender solutions to closed systems of user/context models. The main focus of this work is to provide an architectural model that allows harvesting data from various sources, curating datasets that originate from a multitude of formats and fusing them into semantically enhanced data that contain key performance indicators for the utility of e-Government services. The output can be further processed by analytics and/or recommender engines to suggest public service improvement needs.}},
  author       = {{Sedrakyan, Gayane and De Vocht, Laurens and Alonso, J. and Escalante, M. and Orue-Echevarria, L. and Mannens, Erik}},
  booktitle    = {{PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MODEL-DRIVEN ENGINEERING AND SOFTWARE DEVELOPMENT}},
  isbn         = {{9789897582837}},
  issn         = {{2184-4348}},
  keywords     = {{Architectural Model,Recommendation Generation,Public Administration,Public Services,Data Harvesting,Data Curation,Data Fusion,Linked Data,E-Government}},
  language     = {{eng}},
  location     = {{Madeira, Portugal}},
  pages        = {{691--698}},
  title        = {{Data harvesting, curation and fusion model to support public service recommendations for e-governments}},
  url          = {{http://doi.org/10.5220/0006728206910698}},
  year         = {{2018}},
}
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