A Data Quality in Use model for Big Data
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Preemptive screening and training of data workers produced better quality data than what would typically be achieved through automated post-processing of noisy data.
“For example, Mitra et al. [49] and Thakkar et al. [70] discussed the importance of preparatory measures in the form of screening and training of data workers, with Mitra et al. [49] finding that this preemptive approach produced better quality data than what would typically be achieved through automated post-processing of noisy data.”
From A Survey of Data Quality Requirements That Matter in ML Development Pipelines (2023) · cites this paper as [49]
Cites
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A survey towards an integration of big data analytics to big insights for value-creation
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A Survey of Data Quality Requirements That Matter in ML Development Pipelines
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Data supply chain (DSC): Research synthesis and future directions
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- Authors
- Merino, J., Caballero, I., Rivas, B., Serrano, M., & Piattini, M.
- Affiliations
- University of Castilla-La Mancha (ES)
- Corresponding author
- Merino, Jorge
- Venue
- Future Generation Computer Systems
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 63
- Pages
- 123-130
- ISSN
- 0167-739X (ISSN-L)
- OpenAlex
- W2195118355
- Cited by
- 185
- References
- 23
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 14 · 2023: 23 · 2022: 17 · 2021: 27 · 2020: 18 · 2019: 20 · 2018: 21 · 2017: 27 · 2016: 5 · 2015: 2
- FWCI
- 22.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Future Generation Computer Systems (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Funders
- Ministerio de Economa y Competitividad and Fondo Europeo de Desarrollo Regional FEDER (TIN2012-37493-C03-01)
- Consejera de Educacin, Ciencia y Cultura de la Junta de Comunidades de Castilla La Mancha, y Fondo Europeo de Desarrollo Regional FEDER (PEII11-0327-7035)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- data quality, quality in use, big data, data quality model, business intelligence, data quality management
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