Interoperability-oriented Quality Assessment for Czech Open
Data

D 2022

Interoperability-oriented Quality Assessment for Czech Open Data

KUŠNIRÁKOVÁ, Daša; Mouzhi GE; Leonard WALLETZKÝ a Barbora BÜHNOVÁ

Základní údaje

Originální název

Interoperability-oriented Quality Assessment for Czech Open Data

Autoři

KUŠNIRÁKOVÁ, Daša; Mouzhi GE; Leonard WALLETZKÝ a Barbora BÜHNOVÁ

Vydání

Lisbon, Portugal, Proceedings of the 11th International Conference on Data Science, Technology and Applications, od s. 446-453, 8 s. 2022

Nakladatel

Scitepress

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Označené pro přenos do RIV

Ano

Kód RIV

RIV/00216224:14330/22:00125959

Organizační jednotka

Fakulta informatiky

ISBN

978-989-758-583-8

ISSN

DOI

https://doi.org/10.5220/0011291900003269

UT WoS

000852749000047

Klíčová slova anglicky

Open data; data quality; data interoperability; evaluation framework

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 28. 3. 2023 10:56, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open datasets are usually evaluated based on their metadata only, while leaving the content and the adherence to the recommended data structure to the sole responsibility of the data providers. The interoperability of open datasets remains unknown. This paper therefore aims to propose a novel content-aware quality evaluation framework that assesses the quality of open datasets based on five data quality dimensions. With the proposed framework, we provide a fundamental view on the interoperability-oriented data quality of Czech open datasets, which are published in NODC. Our evaluations find that domain-specific open data quality assessments are able to detect data quality issues beyond traditional heuristics used for determining Czech open data quality, increase their interoperability, and thus increase their potential to bring value for the society. The findings of this research are beneficial not only for the case of the Czech Republic, but also can be applied in other countries that intend to enhance their open data quality evaluation processes.

Návaznosti

CZ.02.1.01/0.0/0.0/16_019/0000822, interní kód MU
(Kód CEP: EF16_019/0000822)

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur (Akronym: C4e)

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur, PO 1 Posilování kapacit pro kvalitní výzkum

EF16_019/0000822, projekt VaV

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

KUŠNIRÁKOVÁ, Daša; Mouzhi GE; Leonard WALLETZKÝ a Barbora BÜHNOVÁ. Interoperability-oriented Quality Assessment for Czech Open Data. Online. In Proceedings of the 11th International Conference on Data Science, Technology and Applications. Lisbon, Portugal: Scitepress, 2022, s. 446-453. ISBN 978-989-758-583-8. Dostupné z: https://doi.org/10.5220/0011291900003269.

@inproceedings{1858841,
   author = {Kušniráková, Daša and Ge, Mouzhi and Walletzký, Leonard and Bühnová, Barbora},
   address = {Lisbon, Portugal},
   booktitle = {Proceedings of the 11th International Conference on Data Science, Technology and Applications},
   doi = {https://doi.org/10.5220/0011291900003269},
   keywords = {Open data; data quality; data interoperability; evaluation framework},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Lisbon, Portugal},
   isbn = {978-989-758-583-8},
   pages = {446-453},
   publisher = {Scitepress},
   title = {Interoperability-oriented Quality Assessment for Czech Open Data},
   url = {https://www.scitepress.org/PublicationsDetail.aspx?ID=UrE+LJsln4A=&t=1},
   year = {2022}
}

TY  - CONF
ID  - 1858841
AU  - Kušniráková, Daša - Ge, Mouzhi - Walletzký, Leonard - Bühnová, Barbora
PY  - 2022
TI  - Interoperability-oriented Quality Assessment for Czech Open Data
PB  - Scitepress
CY  - Lisbon, Portugal
SN  - 9789897585838
KW  - Open data
KW  - data quality
KW  - data interoperability
KW  - evaluation framework
UR  - https://www.scitepress.org/PublicationsDetail.aspx?ID=UrE+LJsln4A=&t=1
N2  - With the rapid increase of published open datasets, it is crucial to support the open data progress in smart cities while considering the open data quality. In the Czech Republic, and its National Open Data Catalogue (NODC), the open datasets are usually evaluated based on their metadata only, while leaving the content and the adherence to the recommended data structure to the sole responsibility of the data providers. The interoperability of open datasets remains unknown. This paper therefore aims to propose a novel content-aware quality evaluation framework that assesses the quality of open datasets based on five data quality dimensions. With the proposed framework, we provide a fundamental view on the interoperability-oriented data quality of Czech open datasets, which are published in NODC. Our evaluations find that domain-specific open data quality assessments are able to detect data quality issues beyond traditional heuristics used for determining Czech open data quality, increase their interoperability, and thus increase their potential to bring value for the society. The findings of this research are beneficial not only for the case of the Czech Republic, but also can be applied in other countries that intend to enhance their open data quality evaluation processes.
ER  -

KUŠNIRÁKOVÁ, Daša; Mouzhi GE; Leonard WALLETZKÝ a Barbora BÜHNOVÁ. Interoperability-oriented Quality Assessment for Czech Open Data. Online. In \textit{Proceedings of the 11th International Conference on Data Science, Technology and Applications}. Lisbon, Portugal: Scitepress, 2022, s.~446-453. ISBN~978-989-758-583-8. Dostupné z: https://doi.org/10.5220/0011291900003269.

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