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Methodologies for data quality assessment and improvement

ACM Computing Surveys · 2009 · Vol. 41(3) · pp. 1–52
Carlo BatiniCinzia CappielloChiara FrancalanciAndrea Maurino

Abstract

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.

Data Quality and ManagementBig Data and Business IntelligenceData Mining Algorithms and ApplicationsComputer scienceData scienceQuality (philosophy)Data qualityPersonalizationSelection (genetic algorithm)Management scienceData miningArtificial intelligenceWorld Wide Web
Citations
1,227
FWCI
56.78
field-weighted impact
References
84
Percentile
100%
vs. same field & year
Citations per year
References
Anchoring data quality dimensions in ontological foundations
Communications of the ACM · 1996 · 1,452 citations
Data quality assessment
Communications of the ACM · 2002 · 1,630 citations
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