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Encyclopedia of Database Systems pp 1063–1068Cite as

Design for Data Quality

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Synonyms

Design for quality; Schema normalization

Definition

The design for data quality (DQ) is the process of designing data artifacts, such as information systems, databases, and data warehouses where data quality issues are considered relevant.

In information systems different types of data are managed; these may be structured such as relational tables in databases, semi-structured data such as XML documents, and unstructured data such as textual documents. Information manufacturing can be seen as the processing system acting on raw data of different types, whose aim is to produce information products. According to this approach, the design for data quality aims to design information-related processes (e.g., creation, updating, and delivering of information) taking into account data quality dimensions.

In the database field, the design for data quality has the objective of producing good (with respect to a given set of quality dimensions) conceptual and relational schemas and...

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Recommended Reading

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

    MATH  Google Scholar 

  2. Dayal U. Query processing in a multidatabase system. In: Kim W, Reiner DS, Batory DS, editors. Query processing in database systems. New York: Springer; 1985. p. 81–108.

    Chapter  Google Scholar 

  3. Jarke M, Jeusfeld MA, Quix C, Vassiliadis P. Architecture and quality in data warehouses: an extended repository approach. Inf Syst. 1999;24(3):229–53.

    Article  Google Scholar 

  4. Jeusfeld MA, Quix C, Jarke M. Design and analysis of quality information for data warehouses. In: Proceedings of the 17th International Conference on Conceptual Modeling; 1998. p. 349–62.

    Chapter  Google Scholar 

  5. Jiang L, Borgida A, Topaloglou T, Mylopoulos J. Data quality by design: a goal-oriented approach. In: Proceedings of the 12th Conference on Information Quality; 2007.

    Google Scholar 

  6. Navathe SB. Evolution of data modeling for databases. Commun ACM. 1992;35(9):112–23.

    Article  Google Scholar 

  7. Shankaranarayanan G, Wang RY, Ziad M. IP-MAP: representing the manufacture of an information product. In: Proceedings of the 5th Conference on Information Quality; 2000.

    Google Scholar 

  8. Storey V, Wang RY. Extending the ER model to represent data quality requirements. In: Wang R, Ziad M, Lee W, editors. Data quality. Boston: Kluwer; 2001.

    Google Scholar 

  9. Storey VC, Wang RY. Modeling quality requirements in conceptual database design. In: Proceedings of the 3rd Conference on Information Quality; 1998. p. 64–87.

    Google Scholar 

  10. Vassiliadis P, Bouzeghoub M, Quix C. Towards quality-oriented data warehouse usage and evolution. In: Proceedings of the 11th Conference on Advanced Information Systems Engineering; 1999. p. 164–79.

    Google Scholar 

  11. Wang RY. A product perspective on total data quality management. Commun ACM. 1998;41(2):58–65.

    Article  Google Scholar 

  12. Wang RY, Kon HB, Madnick SE. Data quality requirements analysis and modeling. In: Proceedings of the 9th International Conference on Data Engineering; 1993. p. 670–77.

    Google Scholar 

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

    Article  Google Scholar 

  14. Wang RY, Storey VC, Firth CP. A framework for analysis of data quality research. IEEE Trans Knowl Data Eng. 1995;7(4):623–40.

    Article  Google Scholar 

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

    MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Carlo Batini & Andrea Maurino

Authors
  1. Carlo Batini
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  2. Andrea Maurino
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Corresponding author

Correspondence to Carlo Batini .

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. Rutgers University, New Brunswick, NJ, USA

    Alexander Borgida

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Batini, C., Maurino, A. (2018). Design for Data Quality. 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_649

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