Skip to main content

Uncertain Schema Matching

Book cover Encyclopedia of Big Data Technologies

Abstract

Data integration has been the focus of research for many years now. At the heart of the data integration process is a schema matching problem whose outcome is a collection of correspondences between different representations of the same real-world construct. In recent years, data integration has been facing new challenges as a result of the presence of big data. These challenges require the development of a set of methods to support a matching process using uncertainty management tools to quantify the inherent uncertainty in the process. This chapter is devoted to the introduction of uncertain schema matching. It also discusses existing and future research, as well as possible applications.

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

Access this chapter

Institutional subscriptions

References

  • Assoudi H, Lounis H (2015) Coping with uncertainty in schema matching: Bayesian networks and agent-based modeling approach. Springer International Publishing, Cham, pp 53–67

    Google Scholar 

  • Batini C, Lenzerini M, Navathe S (1986) A comparative analysis of methodologies for database schema integration. ACM Comput Surv 18(4):323–364

    Article  Google Scholar 

  • Bellahsene Z (2011) Schema matching and mapping. Springer, New York

    Google Scholar 

  • Bergamaschi S, Castano S, Vincini M, Beneventano D (2001) Semantic integration of heterogeneous information sources. Data Knowl Eng 36(3):215–249

    Article  Google Scholar 

  • Berlin J, Motro A (2001) Autoplex: automated discovery of content for virtual databases. Springer, London, pp 108–122

    Chapter  Google Scholar 

  • Bernstein P, Melnik S, Churchill J (2006) Incremental schema matching. Proc Int Conf Very Large Databases 2:1167–1170

    Google Scholar 

  • Bernstein P, Madhavan J, Rahm E (2011) Generic schema matching, ten years later. PVLDB 4(11):695–701

    Google Scholar 

  • Do H, Rahm E (2002) COMA – a system for flexible combination of schema matching approaches. In: Proceedings of the 8th international conference on very data bases, pp 610–621

    Chapter  Google Scholar 

  • Doan A, Domingos P, Halevy A (2001) Reconciling schemas of disparate data sources: a machine-learning approach. In: Proceedings of the ACM SIGMOID international conference on management of data, pp 509–520

    Google Scholar 

  • Doan A, Madhavan J, Domingos P, Halevy A (2002) Learning to map between ontologies on the semantic web. In: Proceedings of the 11th international world wide web conference, pp 662–673

    Google Scholar 

  • Domshlak C, Gal A, Roitman H (2007) Rank aggregation for automatic schema matching. IEEE Trans Knowl Data Eng (TKDE) 19(4):538–553

    Article  Google Scholar 

  • Dong X, Halevy A, Yu C (2009) Data integration with uncertainty. VLDB J 18:469–500

    Article  Google Scholar 

  • Gal A (2006) Managing uncertainty in schema matching with top-k schema mappings. J Data Semant 6:90–114

    Google Scholar 

  • Gal A (2011) Uncertain schema matching. Synthesis lectures on data management. Morgan & Claypool Publishers, San Rafael

    Article  Google Scholar 

  • Gal A, Martinez M, Simari G, Subrahmanian V (2009) Aggregate query answering under uncertain schema mappings, pp 940–951

    Google Scholar 

  • Hung N, Tam N, Miklós Z, Aberer K (2013) On leveraging crowdsourcing techniques for schema matching networks. In: Meng W, Feng L, Bressan S, Winiwarter W, Song W (eds) Database systems for advanced applications. Lecture notes in computer science, vol 7826. Springer, Berlin/Heidelberg, pp 139–154

    Chapter  Google Scholar 

  • Madhavan J, Bernstein P, Rahm E (2001) Generic schema matching with Cupid, pp 49–58

    Google Scholar 

  • Madhavan J, Bernstein P, Domingos P, Halevy A (2002) Representing and reasoning about mappings between domain models, pp 80–86

    Google Scholar 

  • Melnik S, Rahm E, Bernstein P (2003) Rondo: a programming platform for generic model management. In: Proceedings of the ACM SIGMOID international conference on management of data, pp 193–204

    Google Scholar 

  • Miller R, Hernàndez, M, Haas L, Yan LL, Ho C, Fagin R, Popa L (2001) The Clio project: managing heterogeneity. SIGMOD Rec 30(1):78–83

    Article  Google Scholar 

  • Modica G, Gal A, Jamil H (2001) The use of machine-generated ontologies in dynamic information seeking. In: Proceedings of the international conference on cooperative information, pp 433–448

    Chapter  Google Scholar 

  • Ontology alignment evaluation initiative. http://oaei.ontologymatching.org/

  • Rahm E, Bernstein P (2001) A survey of approaches to automatic schema matching. VLDB J 10(4):334–350

    Article  Google Scholar 

  • Sagi T, Gal A (2013) Schema matching prediction with applications to data source discovery and dynamic ensembling. VLDB J 22(5):689–710

    Article  Google Scholar 

  • Sagi T, Gal A (2014) In schema matching, even experts are human: towards expert sourcing in schema matching. In: Workshops proceedings of the 30th international conference on data engineering workshops, ICDE 2014, Chicago, 31 Mar–4 Apr 2014, pp 45–49

    Google Scholar 

  • Sagi T, Gal A (2018) Non-binary evaluation measures for big data integration. VLDB J 27(1):105–126

    Article  Google Scholar 

  • Saha B, Stanoi I, Clarkson K (2010) Schema covering: a step towards enabling reuse in information integration. In: Proceedings of the 26th international conference on data engineering, pp 285–296

    Google Scholar 

  • Saleem K, Bellahsene Z, Hunt E (2007) Performance oriented schema matching. In: Proceedings of the 18th international conference database and expert systems application, pp 844–853

    Google Scholar 

  • Shvaiko P, Euzenat J (2005) A survey of schema-based matching approaches. J Data Semant 4:146–171

    MATH  Google Scholar 

  • Tu K, Yu Y (2005) CMC: combining multiple schema-matching strategies based on credibility prediction. In: Zhou L, Ooi B, Meng X (eds) Database systems for advanced applications. Lecture notes in computer science, vol 3453. Springer, Berlin/Heidelberg, pp 995–995

    Chapter  Google Scholar 

  • Zhang JC, Chen L, Jagadish H, Cao CC (2013) Reducing uncertainty of schema matching via crowdsourcing. Proc VLDB Endow 6:757–768

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Avigdor Gal .

Editor information

Editors and Affiliations

Section Editor information

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Gal, A. (2018). Uncertain Schema Matching. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_24-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63962-8_24-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

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

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Uncertain Schema Matching
    Published:
    15 June 2022

    DOI: https://doi.org/10.1007/978-3-319-63962-8_24-2

  2. Original

    Uncertain Schema Matching
    Published:
    07 March 2018

    DOI: https://doi.org/10.1007/978-3-319-63962-8_24-1