EDP Sciences logo
Submit your paper
Search
Menu
All issues Volume 7 (2022) Renew. Energy Environ. Sustain., 7 (2022) 17 References
Open Access
Issue
Renew. Energy Environ. Sustain.
Volume 7, 2022
Article Number 17
Number of page(s) 12
DOI https://doi.org/10.1051/rees/2022003
Published online 21 June 2022
  1. A. Black, P. van Nederpelt, Dictionary of dimensions of data quality (3DQ) (2020a). [online] Available at: http://www.dama-nl.org/wp-content/uploads/2020/11/3DQ-Dictionary-of-Dimensions-of-Data-Quality-version-1.2-d.d.-14-Nov-2020.pdf [Accessed 24 July 2021] [Google Scholar]
  2. A. Black, P. van Nederpelt, Data concept system for Data Quality Dimensions. Research paper. DAMA-NL (2020) [Google Scholar]
  3. A. Black, P. van Nederpelt, Dimensions of Data Quality Dimensions. Research paper. DAMA-NL (2020) [Google Scholar]
  4. C. Cichy, S. Rass, An overview of data quality frameworks, IEEE Access 7, 24634–24648 (2019) [CrossRef] [Google Scholar]
  5. DAMA, DAMA-DMBOK. Data Management Body of Knowledge 2nd Edition. Technics Publications LLC (2017) [Google Scholar]
  6. DAMA-UK, The six primary dimensions for data quality assessment (2013) [Google Scholar]
  7. DAMA, The DAMA guide to the data management body of knowledge (Technics Publications, Bradley Beach, NJ, 2009) [Google Scholar]
  8. S. Earley, The DAMA Dictionary of Data Management, 2nd Edition (Technics Publications LLC, NJ, 2011) [Google Scholar]
  9. European Statistical System, Quality Assurance Framework of the European Statistical System (2019). Retrieved from ec.europa.eu [Google Scholar]
  10. Federal Committee on Statistical Methodology, A Framework for Data Quality (2020). Available at: https://nces.ed.gov/FCSM/pdf/FCSM.20.04_A_Framework_for_Data_Quality.pdf [Accessed 24 July 2021]. [Google Scholar]
  11. P. Fishburn, Letter to the Editor—Additive utilities with incomplete product sets: application to priorities and assignments, Oper. Res. 15, 537–542 (1967) [CrossRef] [Google Scholar]
  12. C. Fürber, Data Quality Management with Semantic Technologies (Gabler, Wiesbaden, 2016) [Google Scholar]
  13. Government Data Quality Hub, The Government Data Quality Framework (2020). Available at: https://www.gov.uk/government/publications/the-government-data-quality-framework/the-government-data-quality-framework [Accessed 24 July 2021] [Google Scholar]
  14. M. Köksalan, J. Wallenius, S. Zionts, Multiple Criteria Decision Making: From Early History to the 21st Century (World Scientific, Singapore, 2011) [Google Scholar]
  15. D. Loshin, Enterprise Knowledge Management: The Data Quality Approach (Morgan, Boston, MA, 2001) [Google Scholar]
  16. D. Loshin, The Practitioner’s Guide to Data Quality Improvement (Morgan Kaufmann, Boston, MA, 2011) [Google Scholar]
  17. R. Mahanti, Data quality: Dimensions, measurement, strategy, management and governance (ASQ Quality Press, Milwaukee, 2019) [Google Scholar]
  18. P. Ramdasi, S. Salgarkar, A. Kolee, Data in future cities - improving the quality of analytics through simplified data quality assessment framework, SSRN Electr. J. (2019). [Google Scholar]
  19. L. Sebastian-Coleman, Measuring Data Quality for Ongoing Improvement (Morgan Kaufmann, San Francisco, CA, 2013) [Google Scholar]
  20. N.Z. Stats, Census data quality management strategy (2018). Retrieved from www.stats.govt.nz [Google Scholar]
  21. United Nations, The Sustainable Development Goals Report 2019 (2019). Available at: https://unstats.un.org/sdgs/report/2019/The-Sustainable-Development-Goals-Report-2019.pdf [Accessed 24 July 2021]. [Google Scholar]
  22. Y. Wand, R.Y. Wang, Anchoring data quality dimensions in ontological foundations, Commun. ACM 39, 86–95 (1996) [CrossRef] [Google Scholar]
  23. F. Wang, S. Mäs, W. Reinhardt, A. Kandawasvika, Ontology-based quality assurance for mobile data acquisition, in Proceedings of the 19th International Conference on Informatics for Environmental Protection: Networking Environmental Information (2005) [Google Scholar]
  24. R.Y. Wang, A product perspective on total data quality management, Commun. ACM 41, 58–65 (1998) [CrossRef] [Google Scholar]
  25. R.Y. Wang, D.M. Strong, Beyond accuracy: what data quality means to data consumers, J. Manag. Inf. Syst. 12, 5–33 (1996) [CrossRef] [Google Scholar]
  26. R.Y. Wang, D.M. Strong, Beyond accuracy: what data quality means to data consumers, J. Manag. Inf. Syst. 12, 5–33 (1996) [CrossRef] [Google Scholar]
  27. X. Wang, H.J. Hamilton, Y. Bither, An Ontology-Based Approach to Data Cleaning (Technical Report No. 0773105336 9780773105331) (Department of Computer Science, University of Regina, Regina, 2005) [Google Scholar]
  28. X. Wang, X. Sun, F. Cao, L. Ma, N. Kanellos, SMDM: enhancing enterprise-wide master data management using semantic web technologies, VLDB Endowment 2, 1594–1597 (2009) [CrossRef] [Google Scholar]
  29. Y.R. Wang, M. Ziad, Y.W. Lee, Data Quality (Kluwer Academic Publishers, Boston, 2001) [Google Scholar]
  30. M. West, Developing High Quality Data Models (2003) 1–56. Retrieved from https://d2024367-a-62cb3a1a-s-sites.googlegroups.com/site/drmatthewwest/publications/princ03.pdf [Google Scholar]
  31. M. West, Developing High Quality Data Models (Elsevier, 2011) [Google Scholar]
  32. S. Zionts, J. Wallenius, An interactive programming method for solving the multiple criteria problem, Manag. Sci. 22, 652–663 (1976) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.