Skip to main content
SpringerLink
Log in

Data Quality Models

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

  • 140 Accesses

Synonyms

Data quality representations

Definition

Data quality models extend traditional models for databases for the purpose of representing data quality dimensions and the association of such dimensions to data. Therefore, data quality models allow: (i) the analysis of a set of data quality requirements and their representation in terms of conceptual schemas; (ii) accessing and querying data quality dimensions by means of logical schemas. Data quality models also include process models tailored to the analysis and design of quality improvement actions. These models permit tracking data from their source, through various manipulations that data can undergo, to their final usage. In this way, they support the detection of causes of poor data quality and the design of improvement actions.

Historical Background

Among the first data quality models, in 1990 the polygen model [6] was proposed for explicitly tracing the origins of data and the intermediate sources used to arrive at that...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Recommended Reading

  1. Batini C, Scannapieco M. Data quality: concepts, methodologies, and techniques. Berlin: Springer; 2006.

    MATH  Google Scholar 

  2. Scannapieco M, Pernici B, Pierce EM. IP-UML: towards a methodology for quality improvement based on the IP-MAP framework. In: Proceedings of 7th International Conference on Information Quality; 2002.

    Google Scholar 

  3. Scannapieco M, Virgillito A, Marchetti C, Mecella M, Baldoni R. The DaQuinCIS architecture: a platform for exchanging and improving data quality in cooperative information systems. Inf Syst. 2004;29(7):551–82.

    Article  Google Scholar 

  4. Shankaranarayan G, Wang RY, Ziad M. Modeling the manufacture of an information product with IP-MAP. In: Proceedings of 5th International Conference on Information Quality; 2000. p. 1–16.

    Google Scholar 

  5. Storey VC, Wang RY. An analysis of quality requirements in database design. In: Proceedings of 4th International Conference on Information Quality; 1998. p. 64–87.

    Google Scholar 

  6. Wang RY, Madnick SE. A polygen model for heterogeneous database systems: the source tagging perspective. In: Proceedings of 16th International Conference on Very Large Data Bases; 1990. p. 519–38.

    Google Scholar 

  7. Wang RY, Reddy MP, Kon H. Toward data quality: an attribute-based approach. Decis Support Syst. 1995;13(3–4):349–72.

    Article  Google Scholar 

  8. Wang RY, Ziad M, Lee YW. Data quality. Boston: Kluwer; 2001.

    MATH  Google Scholar 

  9. W3C Working Group. An overview of the PROV family of documents, available at: http://www.w3.org/TR/prov-overview/

Download references

Author information

Authors and Affiliations

  1. University of Rome, Rome, Italy

    Monica Scannapieco

Authors
  1. Monica Scannapieco
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Monica Scannapieco .

Editor information

Editors and Affiliations

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

    Ling Liu

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

    M. Tamer Özsu

Section Editor information

  1. Information Systems, Hasso-Plattner-Institute, Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany

    Felix Naumann

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media New York

About this entry

Cite this entry

Scannapieco, M. (2016). Data Quality Models. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_109-2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_109-2

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation