Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Data Scrubbing

  • Peter ChristenEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_80621


Data cleansing


Data scrubbing refers to the task of first identifying data that are corrupted, incomplete, invalid, missing, inconsistent, outdated, duplicated, or irrelevant and then either correcting or removing such “dirty” data. The aim of data scrubbing is to make data more accurate, more complete, and consistent both within and across different tables in a database or data warehouse.

An important challenge of data scrubbing is that “dirty” values do not necessarily contradict any database requirements, i.e., such values are consistent with the design of a database and its schema. Rather, errors occur at a higher conceptual level. Examples include credit card numbers that follow a correct grouping of four-times-four digits but that are invalid with regard to a check-sum algorithm, or addresses that have a valid zipcode value that is inconsistent with the town and state names in the same record. Such errors can occur because of a lack of checks and validation...

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Recommended Reading

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    Batini C, Scannapieco M. Data quality: concepts, methodologies and techniques, Data-centric systems and applications. Berlin: Springer; 2006.zbMATHGoogle Scholar
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    Christen P. Data matching – concepts and techniques for record linkage, entity resolution, and duplicate detection, Data-centric systems and applications. Berlin: Springer; 2012.Google Scholar
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    Fan W, Geerts F, Jia X, Kementsietsidis A. Conditional functional dependencies for capturing data inconsistencies. ACM Trans Database Syst. 2008;33(2):6.CrossRefGoogle Scholar
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    Lee Y, Pipino L, Funk J, Wang R. Journey to data quality. Cambridge, MA: The MIT Press; 2009.Google Scholar
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    Maletic JI, Marcus A. Data cleansing: a prelude to knowledge discovery. In: Data mining and knowledge discovery handbook. New York: Springer; p. 19–32.CrossRefGoogle Scholar
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    Rahm E, Do HH. Data cleaning: problems and current approaches. IEEE Data Eng Bull. 2000;23(4):3–13.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research School of Computer ScienceThe Australian National UniversityCanberraAustralia

Section editors and affiliations

  • Felix Naumann
    • 1
  1. 1.Information SystemsHasso-Plattner-InstitutePotsdamGermany