{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T14:17:44Z","timestamp":1791814664870,"version":"4.3.4"},"reference-count":17,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,6,5]],"date-time":"2020-06-05T00:00:00Z","timestamp":1591315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>Researchers need to be able to integrate ever-increasing amounts of data into their institutional databases, regardless of the source, format, or size of the data. It is then necessary to use the increasing diversity of data to derive greater value from data for their organization. The processing of electronic data plays a central role in modern society. Data constitute a fundamental part of operational processes in companies and scientific organizations. In addition, they form the basis for decisions. Bad data quality can negatively affect decisions and have a negative impact on results. The quality of the data is crucial. This includes the new theme of data wrangling, sometimes referred to as data munging or data crunching, to find the dirty data and to transform and clean them. The aim of data wrangling is to prepare a lot of raw data in their original state so that they can be used for further analysis steps. Only then can knowledge be obtained that may bring added value. This paper shows how the data wrangling process works and how it can be used in database systems to clean up data from heterogeneous data sources during their acquisition and integration.<\/jats:p>","DOI":"10.3390\/data5020050","type":"journal-article","created":{"date-parts":[[2020,6,9]],"date-time":"2020-06-09T04:19:39Z","timestamp":1591676379000},"page":"50","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Data Wrangling in Database Systems: Purging of Dirty Data"],"prefix":"10.3390","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5225-389X","authenticated-orcid":false,"given":"Otmane","family":"Azeroual","sequence":"first","affiliation":[{"name":"German Center for Higher Education Research and Science Studies (DZHW), Sch\u00fctzenstra\u00dfe 6a, Berlin 10117, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1080\/07421222.1996.11518099","article-title":"Beyond accuracy: What data quality means to data consumers","volume":"12","author":"Wang","year":"1996","journal-title":"J. Manag. Inf. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Azeroual, O., Saake, G., and Abuosba, M. (2019). ETL best practices for data quality checks in RIS databases. Informatics, 6.","DOI":"10.3390\/informatics6010010"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s12525-011-0062-2","article-title":"Information and data quality in networked business","volume":"21","author":"Otto","year":"2011","journal-title":"Electron. Mark."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1145\/269012.269025","article-title":"The impact of poor data quality on the typical entreprise","volume":"41","author":"Redman","year":"1998","journal-title":"Commun. ACM"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/253769.253804","article-title":"Data quality in context","volume":"40","author":"Strong","year":"1997","journal-title":"Commun. ACM"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1145\/269012.269022","article-title":"A product perspective on total data quality management","volume":"41","author":"Wang","year":"1998","journal-title":"Commun. ACM"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ifacol.2015.05.197","article-title":"Data Wrangling: Making data useful again","volume":"48","author":"Endel","year":"2015","journal-title":"IFAC-PapersOnLine"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1177\/1473871611415994","article-title":"Research directions in data wrangling: Visualizations and transformations for usable and credible data","volume":"10","author":"Kandel","year":"2011","journal-title":"J. Inf. Vis."},{"key":"ref_9","first-page":"12","article-title":"Data quality measures and data cleansing for research information systems","volume":"16","author":"Azeroual","year":"2018","journal-title":"J. Digit. Inf. Manag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ijinfomgt.2018.02.007","article-title":"Analyzing data quality issues in research information systems via data profiling","volume":"41","author":"Azeroual","year":"2018","journal-title":"Int. J. Inf. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kandel, S., Paepcke, A., Hellerstein, J., and Heer, J. (2011, January 7\u201312). Wrangler: Interactive visual specification of data transformation scripts. Proceedings of the International Conference on Human Factors in Computing Systems, CHI 2011, Vancouver, BC, Canada.","DOI":"10.1145\/1978942.1979444"},{"key":"ref_12","unstructured":"McCallum, Q.E. (2012). Bad Data Handbook, O\u2019Reilly Media."},{"key":"ref_13","unstructured":"Rud, O.P. (2001). Data Mining Cookbook: Modeling Data for Marketing, Eisk and Customer Eelationship Management, Wiley."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Leonelli, S. (2019). Data Governance is Key to Interpretation: Reconceptualizing Data in Data Science. Harv. Data Sci. Rev., 1.","DOI":"10.1162\/99608f92.17405bb6"},{"key":"ref_15","unstructured":"William, L., Ranjeev, M., Donald, S., Ira, S.S.M., and Stephen, R. (2019). Artificial Intelligence for the Internet of Everything, Elsevier."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xie, I., and Matusiak, K. (2016). Chapter 11 \u2013 New developments and challenges. Discover Digital Libraries, Elsevier.","DOI":"10.1016\/B978-0-12-417112-1.00011-9"},{"key":"ref_17","unstructured":"Rattenbury, T., Hellerstein, J., Heer, J., Kandel, S., and Carreras, C. (2017). Principles of Data Wrangling: Practical Techniques for Data Preparation, O\u2019Reilly Media. [1st ed.]."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/2\/50\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:35:51Z","timestamp":1760175351000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/5\/2\/50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,5]]},"references-count":17,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["data5020050"],"URL":"https:\/\/doi.org\/10.3390\/data5020050","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,5]]}}}