Skip to main content

Managing Data Integration Uncertainty

  • Reference work entry
  • First Online:
Encyclopedia of Database Systems
  • 19 Accesses

Synonyms

Probabilistic schema alignment

Definition

Consider a set of source schemas \({\mathcal {S}} = \{S_1, \dots , S_n\}\) in the same domain, where different schemas may describe the domain in different ways. An important component in data integration is schema alignment, including three steps: (1) creating a mediated schema M that provides a unified and virtual view of the disparate sources and captures the salient aspects of the domain being considered, (2) generating attribute matching that matches attributes in each source schema Si, i ∈ [1, n], to the corresponding attributes in the mediated schema M, and (3) building a schema mapping between each source schema Si and the mediated schema Mto specify the semantic relationships between the contents of the source and that of the mediated data. The result schema mappings are used to reformulate a user query into a set of queries on the underlying data sources for query answering. Uncertainty can arise in every step of schema...

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
Price excludes VAT (USA)
  • Durable hardcover 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

Recommended Reading

  1. Franklin M, Halevy AY, Maier D. From databases to dataspaces: a new abstraction for information management. Sigmod Rec. 2005;34(4):27–33.

    Article  Google Scholar 

  2. Sarma AD, Dong XL, Halevy A. Bootstrapping pay-as-you-go data integration systems. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2008. p. 861–74.

    Google Scholar 

  3. Dong X, Halevy AY, Yu C. Data integration with uncertainties. In: Proceedings of the 33rd International Conference on Very Large Data Bases; 2007. p. 687–98.

    Google Scholar 

  4. Gal A, Anaby-Tavor A, Trombetta A, Montesi D. A framework for modeling and evaluating automatic semantic reconciliation. VLDB J. 2003;14:50–67.

    Article  Google Scholar 

  5. Gal A, Martinez MV, Simari GI, Subrahmanian VS. Aggregate query answering under uncertain schema mappings. In: Proceedings of the 25th International Conference on Data Engineering; 2009. p. 940–51.

    Google Scholar 

  6. Dong XL, Gabrilovich E, Heitz G, Horn W, Lao N, Murphy K, et al. Knowledge vault: a web-scale approach to probabilistic knowledge fusion. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2014.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Alon Halevy .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Dong, X.L., Halevy, A. (2018). Managing Data Integration Uncertainty. 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_80743

Download citation

Publish with us

Policies and ethics