Skip to main content
SpringerLink
Log in

A Data Quality Framework for Graph-Based Virtual Data Integration Systems

  • Conference paper
  • First Online:
Advances in Databases and Information Systems (ADBIS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13389))

Included in the following conference series:

  • 772 Accesses

Abstract

Data Quality (DQ) plays a critical role in data integration. Up to now, DQ has mostly been addressed from a single database perspective. Popular DQ frameworks rely on Integrity Constraints (IC) to enforce valid application semantics, which lead to the Denial Constraint (DC) formalism which models a broad range of ICs in real-world applications. Yet, current approaches are rather monolithic, considering a single database and do not suit data integration scenarios. In this paper, we address DQ for data integration systems. Specifically, we extend virtual data integration systems to elicit DCs from disparate data sources to be integrated, using DC-related state-of-the-art, and propagate them to the integrated schema (global DCs). Then, we propose a method to manage global DCs and identify (i) minimal DCs and (ii) potential clashes between them.

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
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abedjan, Z., et al.: Detecting data errors: where are we and what needs to be done? Proc. VLDB Endow. 9(12), 993–1004 (2016)

    Article  Google Scholar 

  2. Batini, C., Rula, A.: From data quality to big data quality: a data integration scenario. In: SEBD, Volume 2994 of CEUR Workshop Proceedings, pp. 36–47. CEUR-WS.org (2021)

    Google Scholar 

  3. Batini, C., Rula, A., Scannapieco, M., Viscusi, G.: From data quality to big data quality. J. Database Manag. 26(1), 60–82 (2015)

    Article  Google Scholar 

  4. Bleifuß, T., Kruse, S., Naumann, F.: Efficient denial constraint discovery with hydra. Proc. VLDB Endow. 11(3), 311–323 (2017)

    Article  Google Scholar 

  5. Chomicki, J., Marcinkowski, J.: Minimal-change integrity maintenance using tuple deletions. Inf. Comput. 197(1–2), 90–121 (2005)

    Article  MathSciNet  Google Scholar 

  6. Chu, X., Ilyas, I.F., Papotti, P.: Discovering denial constraints. Proc. VLDB Endow. 6(13), 1498–1509 (2013)

    Article  Google Scholar 

  7. Geerts, F., Mecca, G., Papotti, P., Santoro, D.: Cleaning data with Llunatic. VLDB J. 29(4), 867–892 (2020). https://doi.org/10.1007/s00778-019-00586-5

    Article  Google Scholar 

  8. Halevy, A.Y.: Answering queries using views: a survey. VLDB J. 10(4), 270–294 (2001). https://doi.org/10.1007/s007780100054

    Article  MATH  Google Scholar 

  9. Haug, A., Zachariassen, F., Van Liempd, D.: The costs of poor data quality. J. Ind. Eng. Manag. (JIEM) 4(2), 168–193 (2011)

    Google Scholar 

  10. Heidari, A., McGrath, J., Ilyas, I.F., Rekatsinas, T.: HoloDetect: few-shot learning for error detection. In: SIGMOD Conference, pp. 829–846. ACM (2019)

    Google Scholar 

  11. Jarke, M., Jeusfeld, M.A., Quix, C., Vassiliadis, P.: Architecture and quality in data warehouses. In: Pernici, B., Thanos, C. (eds.) CAiSE 1998. LNCS, vol. 1413, pp. 93–113. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0054221

    Chapter  Google Scholar 

  12. Kolaitis, P.G.: Schema mappings, data exchange, and metadata management. In: PODS, pp. 61–75. ACM (2005)

    Google Scholar 

  13. Laranjeiro, N., Soydemir, S.N., Bernardino, J.: A survey on data quality: classifying poor data. In: PRDC, pp. 179–188. IEEE Computer Society (2015)

    Google Scholar 

  14. Livshits, E., Heidari, A., Ilyas, I.F., Kimelfeld, B.: Approximate denial constraints. Proc. VLDB Endow. 13(10), 1682–1695 (2020)

    Article  Google Scholar 

  15. Loshin, D.: Evaluating the business impacts of poor data quality. Inf. Qual. J. (2011)

    Google Scholar 

  16. Nadal, S., Abello, A., Romero, O., Vansummeren, S., Vassiliadis, P.: Graph-driven federated data management. IEEE Trans. Knowl. Data Eng. (2021)

    Google Scholar 

  17. Pena, E.H.M., de Almeida, E.C., Naumann, F.: Discovery of approximate (and exact) denial constraints. Proc. VLDB Endow. 13(3), 266–278 (2019)

    Article  Google Scholar 

  18. Rekatsinas, T., Chu, X., Ilyas, I.F., Ré, C.: HoloClean: holistic data repairs with probabilistic inference. Proc. VLDB Endow. 10(11), 1190–1201 (2017)

    Article  Google Scholar 

  19. Sadiq, S.W., Papotti, P.: Big data quality - whose problem is it? In: ICDE, pp. 1446–1447. IEEE Computer Society (2016)

    Google Scholar 

  20. Schirmer, P., et al.: DynFD: functional dependency discovery in dynamic datasets. In: EDBT, pp. 253–264. OpenProceedings.org (2019)

    Google Scholar 

  21. Xiao, G., et al.: Ontology-based data access: a survey. In: IJCAI, pp. 5511–5519. ijcai.org (2018)

    Google Scholar 

Download references

Acknowledgements

This work was partly supported by the DOGO4ML project, funded by the Spanish Ministerio de Ciencia e Innovación under project PID2020-117191RB-I00. Sergi Nadal is partly supported by the Spanish Ministerio de Ciencia e Innovación, as well as the European Union - NextGenerationEU, under project FJC2020-045809-I.

Author information

Authors and Affiliations

  1. Universitat Politècnica de Catalunya (BarcelonaTech), Barcelona, Spain

    Yalei Li, Sergi Nadal & Oscar Romero

Authors
  1. Yalei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sergi Nadal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Oscar Romero
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergi Nadal .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Silvia Chiusano

  2. Politecnico di Torino, Turin, Italy

    Tania Cerquitelli

  3. Poznań University of Technology, Poznań, Poland

    Robert Wrembel

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Y., Nadal, S., Romero, O. (2022). A Data Quality Framework for Graph-Based Virtual Data Integration Systems. In: Chiusano, S., Cerquitelli, T., Wrembel, R. (eds) Advances in Databases and Information Systems. ADBIS 2022. Lecture Notes in Computer Science, vol 13389. Springer, Cham. https://doi.org/10.1007/978-3-031-15740-0_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-15740-0_9

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-15739-4

  • Online ISBN: 978-3-031-15740-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation