Data Quality Dimensions

Nasr, Mona; Shaaban, Essam; Gabr, Menna Ibrahim

doi:10.1007/978-981-15-3075-3_14

Cited by 2 publications

(2 citation statements)

References 44 publications

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“…Data quality is characterized as "the measure of the agreement between the data views presented by an information system and that same data in the real world" [58,59]. Data quality has multifaceted aspects [60] such as accuracy, completeness, consistency, and currency [61,62]. In the VGI context, data quality is described as the quality and accuracy of the VGI dataset.…”

Section: Technology Contextmentioning

confidence: 99%

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Munir

Khayal

Tahir

2022

IJGI

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Spatial data infrastructures (SDIs) have been implemented for the last four decades in most countries. One of the key objectives of SDIs is to ensure the quick availability and accessibility of spatial data. The success of SDI depends on the underlying spatial datasets. Many developing countries such as Pakistan are facing problems in implementing SDI because of the unavailability of spatial data. Volunteered Geographic Information (VGI) is an alternate source for obtaining spatial data. Therefore, the question is what factors hamper the adoption of VGI for making it part of SDI in Pakistan. The intention behind this paper is to explore such factors as the key research question. To do so, we make use of the Technology–Organization–Environment (TOE) framework along with the partial least square structural equation model (PLS-SEM) to empirically analyze the factors impeding VGI from becoming part of SDI in the country. The study concludes that many technical, organizational, and environmental factors affect the adoption of VGI to be part of SDI in Pakistan.

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Section: Technology Contextmentioning

confidence: 99%

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Munir

Khayal

Tahir

2022

IJGI

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“…Normalmente há setores especıficos para essa tarefa relacionada ao controle de qualidade (CQ) [Knauer et al 2020]. O conceito de dimensões em qualidade de dados está relacionado à identificac ¸ão de medidas de qualidade relacionadas a elementos de dados, incluindo atributos, registros, tabelas, sistemas ou agrupamentos mais abstratos, como unidades de negócios, empresas ou gamas de produtos [Nasr et al 2020]. Monitorar a qualidade das bases de dados depende das dimensões que serão monitoradas.…”

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Analisador de Estruturas de Bases de Dados para o Agronegócio com Dimensões de Qualidade de Dados

Clovis

Dorneles

2021

Anais Estendidos Do XXXVI Simpósio Brasileiro De Banco De Dados (SBBD Estendido 2021)

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Este artigo apresenta uma ferramenta para análise da qualidade de dados de bases de dados, cujo objetivo é fornecer uma interface para auxiliar na verificação e análise dos dados. A solução foi desenvolvida combinando recursos de dicionário de dados e dos dados armazenados possibilitando a investigação e análise de aspectos relevantes para análise baseada em critérios de dimensões de qualidade de dados. Com isso, pretende-se fornecer resultados qualitativos referentes à estrutura na qual os dados estão armazenados indicando características inadequadas como integridade, objetividade e validade entre outras dimensões

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Data Quality Dimensions

Cited by 2 publications

References 44 publications

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Analysis of Factors Affecting Adoption of Volunteered Geographic Information in the Context of National Spatial Data Infrastructure

Analisador de Estruturas de Bases de Dados para o Agronegócio com Dimensões de Qualidade de Dados

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