The DOI matches its Crossref record; 56 of the 56 references it cites were found in a registry or an index.
Methodologies for data quality assessment and improvement
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.
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Cites
Lecture Notes in Computer Science 1205
(1999) · Industrial Robot: An International Journal · doi:10.1108/ir.1999.04926fae.001 · Cited by 38,662
Reengineering the corporation: A manifesto for business revolution
Hammer, M., & Champy, J. (1993) · Business Horizons · doi:10.1016/s0007-6813(05)80064-3 · Cited by 6,455
Beyond Accuracy: What Data Quality Means to Data Consumers
Wang, R. Y., & Strong, D. M. (1996) · Journal of Management Information Systems · doi:10.1080/07421222.1996.11518099 · Cited by 4,672
Reengineering Work: Don’t Automate, Obliterate
Hammer, M. M. (1990) · Cited by 2,908
Hammer, M. M., & Champy, J. A. (1993) · Cited by 2,519
Data integration: a theoretical perspective
Lenzerini, M. (2002) · Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems · doi:10.1145/543613.543644 · Cited by 2,494
Pipino, L. L., Lee, Y. W., & Wang, R. Y. (2002) · Communications of the ACM · doi:10.1145/505248.506010 · Cited by 1,724
AIMQ: a methodology for information quality assessment
Lee, Y. W., Strong, D. M., Kahn, B. K., & Wang, R. Y. (2002) · Information & Management · doi:10.1016/s0378-7206(02)00043-5 · Cited by 1,611
Anchoring Data Quality Dimensions in Ontological Foundations
Wand, Y., & Wang, R. Y. (1996) · Communications of the ACM · doi:10.1145/240455.240479 · Cited by 1,479
Data on the Web from relations to semistructured data and XML
Abiteboul, S., Buneman, P., & Suciu, D. M. (2000) · Cited by 1,062
Cited by
Hazen, B. T., Boone, C. A., Ezell, J. D., & Jones-Farmer, L. A. (2014) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.04.018 · Cited by 863
Zuiderwijk, A., Chen, Y.-C., & Salem, F. (2021) · Government Information Quarterly · doi:10.1016/j.giq.2021.101577 · Cited by 795
Zaveri, A., Rula, A., Maurino, A., Pietrobon, R., Lehmann, J., & Auer, S. (2015) · Semantic Web · doi:10.3233/sw-150175 · Cited by 596
Self-citationA Review of Data Quality Assessment Methods for Public Health Information Systems
Chen, H., Hailey, D., Wang, N., & Yu, P. (2014) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph110505170 · Cited by 354
Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO
Färber, M., Bartscherer, F., Menne, C., & Rettinger, A. (2017) · Semantic Web · doi:10.3233/sw-170275 · Cited by 346
Metadata
- Authors
- Batini, C., Cappiello, C., Francalanci, C., & Maurino, A.
- Venue
- ACM Computing Surveys
- Published
- 2009-07
- Volume
- 41
- Issue
- 3
- Pages
- 1-52
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2004291985
- Cited by
- 1,288
- References
- 56
- FWCI
- 60.44
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
