Skip to main content
Springer Nature Link
Log in

Modeling Context for Data Quality Management

  • Conference paper
  • First Online:
Conceptual Modeling (ER 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13607))

Included in the following conference series:

  • 1044 Accesses

Abstract

The importance of context for data quality (DQ) has been shown decades ago and is widely accepted. Early approaches and surveys defined DQ as fitness for use and showed the influence of context on DQ. However, very few proposals for context modeling can be found in DQ literature. This paper reviews many context components suggested in the literature and proposes a context model tailored for DQ management. Through a running example, and relying on the literature reviewed, we illustrate the applicability of the model.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.snomed.org/.

  2. 2.

    Research project for the e-Government Agency and Information and Knowledge Society (AGESIC) in Uruguay, https://www.gub.uy/agencia-gobierno-electronico-sociedad-informacion-conocimiento/.

References

  1. Akram, M., Malik, A.: Evaluating citizens’ readiness to embrace e-government services. In: Proceedings of the 13th Annual International Conference on Digital Government Research, pp. 58–67 (2012)

    Google Scholar 

  2. Bertossi, L., Rizzolo, F., Jiang, L.: Data quality is context dependent. In: Castellanos, M., Dayal, U., Markl, V. (eds.) BIRTE 2010. LNBIP, vol. 84, pp. 52–67. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22970-1_5

    Chapter  Google Scholar 

  3. Davoudian, A., Liu, M.: Big data systems: a software engineering perspective. ACM 53(5), 1–39 (2020)

    Google Scholar 

  4. Dey, A.: Understanding and using context. PUC 5(1), 4–7 (2001)

    Google Scholar 

  5. Etcheverry, L., et al.: Qbox-foundation: a metadata platform for quality measurement. In: Proceeding of the 4th Workshop on Data and Knowledge Quality (2008)

    Google Scholar 

  6. Fürber, C.: Data Quality Management with Semantic Technologies, chap. Data Quality. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-658-12225-6

  7. Görz, Q., Kaiser, M.: An indicator function for insufficient data quality – a contribution to data accuracy. In: Rahman, H., Mesquita, A., Ramos, I., Pernici, B. (eds.) MCIS 2012. LNBIP, vol. 129, pp. 169–184. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33244-9_12

    Chapter  Google Scholar 

  8. Marotta, A., Vaisman, A.: Rule-based multidimensional data quality assessment using contexts. In: Madria, S., Hara, T. (eds.) DaWaK 2016. LNCS, vol. 9829, pp. 299–313. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43946-4_20

    Chapter  Google Scholar 

  9. Merino, J., et al.: A data quality in use model for big data. FGCS 63, 123–130 (2016)

    Google Scholar 

  10. Serra, F., Marotta, A.: Data warehouse quality assessment using contexts. In: Cellary, W., Mokbel, M.F., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds.) WISE 2016. LNCS, vol. 10042, pp. 436–448. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48743-4_36

    Chapter  Google Scholar 

  11. Serra, F., et al.: Use of context in data quality management: a systematic literature review (2022). arxiv.org/abs/2204.10655

  12. Strong, D., et al.: Data quality in context. CACM 40(5), 103–110 (1997)

    Google Scholar 

  13. Todoran, I., et al.: A methodology to evaluate important dimensions of information quality in systems. JDIQ 6(2-3), 1–23 (2015)

    Google Scholar 

  14. Visengeriyeva, L., Abedjan, Z.: Anatomy of metadata for data curation. JDIQ 12(3) (2020)

    Google Scholar 

  15. Wang, R., Strong, D.: Beyond accuracy: what data quality means to data consumers. JMIS 12(4), 5–33 (1996)

    Google Scholar 

  16. Wang, J., et al.: An ontology-based quality framework for data integration. In: Workshops on Business Informatics Research, pp. 196–208 (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Universidad de la República, Montevideo, Uruguay

    Flavia Serra & Adriana Marotta

  2. Université de Tours, Blois, France

    Flavia Serra, Verónika Peralta & Patrick Marcel

Authors
  1. Flavia Serra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Verónika Peralta
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adriana Marotta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patrick Marcel
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Flavia Serra .

Editor information

Editors and Affiliations

  1. University of Geneva, Carouge, Switzerland

    Jolita Ralyté

  2. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  3. IIIT Delhi, New Delhi, India

    Mukesh Mohania

  4. University of Skövde, Skövde, Sweden

    Manfred A. Jeusfeld

  5. International Institute of Information Technology Gachibowli, Hyderabad, India

    Kamalakar Karlapalem

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Serra, F., Peralta, V., Marotta, A., Marcel, P. (2022). Modeling Context for Data Quality Management. In: Ralyté, J., Chakravarthy, S., Mohania, M., Jeusfeld, M.A., Karlapalem, K. (eds) Conceptual Modeling. ER 2022. Lecture Notes in Computer Science, vol 13607. Springer, Cham. https://doi.org/10.1007/978-3-031-17995-2_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-17995-2_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17994-5

  • Online ISBN: 978-3-031-17995-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics