Handling Context in Data Quality Management

Serra, Flavia

doi:10.1007/978-3-030-55814-7_32

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Moreover, [83] studies the mapping between metadata and DQ issues, in particular, the connection between metadata (such as count of rows, count of nulls, count of values and count of value pattern), and data errors. In other matters, [47,71] apply a systematic literature review, the former to find works that consider DQ requirements during the process of developing an information system, the latter presents our preliminary results of review on DQ and context. In addition, in [9] the authors compare methodologies that have been proposed in the literature for information quality assessment and improvement.…”

Section: Review Resultsmentioning

confidence: 99%

Use of Context in Data Quality Management: a Systematic Literature Review

Serra¹,

Peralta²,

Marotta³

et al. 2022

Preprint

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The importance of context in data quality (DQ) was shown many years ago and nowadays is widely accepted. Early approaches and surveys defined DQ as fitness for use and showed the influence of context on DQ. This paper presents a Systematic Literature Review (SLR) for investigating how context is taken into account in recent proposals for DQ management. We specifically present the planning and execution of the SLR, the analysis criteria and our results reflecting the relationship between context and DQ in the state of the art and, particularly, how that context is defined and used for DQ management.CCS Concepts: • Data Quality → Context in Data Quality Management.

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Section: Review Resultsmentioning

confidence: 99%

Use of Context in Data Quality Management: a Systematic Literature Review

Serra¹,

Peralta²,

Marotta³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The research described in [26] deals with Data Quality Management (DQM). Data Quality naturally depends on the application context and usage needs.…”

Section: Selected Papersmentioning

confidence: 99%

Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

Bellatreche

Bentayeb

Bieliková

et al. 2020

ADBIS, TPDL and EDA 2020 Common Workshops and Doctoral Consortium

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Use of Context in Data Quality Management: a Systematic Literature Review

SERRA,

Peralta,

Marotta

et al. 2024

J. Data and Information Quality

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The importance of context in data quality (DQ) was shown many years ago and nowadays is widely accepted. Early approaches and surveys defined DQ as fitness for use and showed the influence of context on DQ. This paper presents a Systematic Literature Review (SLR) for investigating how context is taken into account in recent proposals for DQ management (DQM). We specifically present the planning and execution of the SLR, the analysis criteria and our results reflecting the relationship between context and DQ in the state of the art and, particularly, how this context is defined and used for DQM. The SLR is instrumental to the identification of context components and the design of a context formal model.

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Handling Context in Data Quality Management

Cited by 3 publications

References 20 publications

Use of Context in Data Quality Management: a Systematic Literature Review

Use of Context in Data Quality Management: a Systematic Literature Review

Databases and Information Systems in the AI Era: Contributions from ADBIS, TPDL and EDA 2020 Workshops and Doctoral Consortium

Use of Context in Data Quality Management: a Systematic Literature Review

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