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Encyclopedia of Database Systems pp 808–812Cite as

Data Profiling

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Synonyms

Database profiling

Definition

Data profiling refers to the activity of creating small but informative summaries of a database [1]. These summaries range from simple statistics such as the number of records in a table and the number of distinct values of a field, to more complex statistics such as the distribution of n-grams in the field text, to structural properties such as keys and functional dependencies. Database profiles are useful for database exploration, detection of data quality problems [2], and for schema matching in data integration [1]. Database exploration helps a user identify important database properties, whether it is data of interest or data quality problems. Schema matching addresses the critical question, “do two fields or sets of fields or tables represent the same information?” Answers to these questions are very useful for designing data integration scripts.

Historical Background

Databases which support a complex organization tend to be quite complex...

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Recommended Reading

  1. Evoke Software. Data profiling and mapping, the essential first step in data migration and integration projects. Available at: http://www.evokesoftware.com/pdf/wtpprDPM.pdf (2000).

  2. Dasu T, Johnson T, Muthukrishnan S, Shkapenyuk V. Mining database structure; or, how to build a data quality browser. In: Proceedings of the ACM SIGMOD International Conference on Management of data; 2002. p. 240–51.

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  3. Dasu T, Johnson T. Exploratory data mining and data cleaning. New York: Wiley Interscience; 2003.

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  4. Kang J, Naughton JF. On schema matching with opaque column names and data values. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2003. p. 205–16.

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  5. Broder A. On the resemblance and containment of documents. In: Proceedings of the IEEE Conference on Compression and Comparison of Sequences; 1997. p. 21–9.

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  6. Dasu T, Johnson T, Marathe A. Database exploration using database dynamics. IEEE Data Eng Bull. 2006;29(2):43–59.

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  7. Gravano L, Ipeirotis PG, Jagadish HV, Koudas N, Muthukrishnan S, Srivastava D. Approximate String Joins in a Database (Almost) for Free. In: Proceedings of the 27th International Conference on Very Large Data Bases; 2001. p. 491–500.

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  8. Huhtala Y, Karkkainen J, Porkka P, Toivonen H. TANE: an efficient algorithm for discovering functional and approximate dependencies. Comp J. 1999;42(2):100–11.

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  9. Shen W, DeRose P, Vu L, Doan AH, Ramakrishnan R. Source-aware entity matching: a compositional approach. In: Proceedings of the 23rd International Conference on Data Engineering. p. 196–205.

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  10. IBM Websphere Information Integration. Available at: http://ibm.ascential.com

  11. Informatica Data Explorer. Available at: http://www.informatica.com/products_services/data_explorer

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Author information

Authors and Affiliations

  1. AT&T Labs – Research, Florham Park, NJ, USA

    Theodore Johnson

Authors
  1. Theodore Johnson
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Corresponding author

Correspondence to Theodore Johnson .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. Microsoft Research, Microsoft Corporation, Redmond, WA, USA

    Venkatesh Ganti

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Johnson, T. (2018). Data Profiling. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_601

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