Advertisement

Matching and Alignment: What Is the Cost of User Post-Match Effort?

(Short Paper)
  • Fabien Duchateau
  • Zohra Bellahsene
  • Remi Coletta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7044)

Abstract

Generating new knowledge from scientific databases, fusioning products information of business companies or computing an overlap between various data collections are a few examples of applications that require data integration. A crucial step during this integration process is the discovery of correspondences between the data sources, and the evaluation of their quality. For this purpose, the overall metric has been designed to compute the post-match effort, but it suffers from major drawbacks. Thus, we present in this paper two related metrics to compute this effort. The former is called post-match effort, i.e., the amount of work that the user must provide to correct the correspondences that have been discovered by the tool. The latter enables the measurement of human-spared resources, i.e., the rate of automation that has been gained by using a matching tool.

Keywords

User Interaction Schema Match Ontology Match Large Schema Matching Tool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Smith, K., Morse, M., Mork, P., Li, M., Rosenthal, A., Allen, D., Seligman, L.: The role of schema matching in large enterprises. In: CIDR (2009)Google Scholar
  2. 2.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB Journal 10(4), 334–350 (2001)CrossRefzbMATHGoogle Scholar
  3. 3.
    Yatskevich, M.: Preliminary evaluation of schema matching systems. Technical Report DIT-03-028, Informatica e Telecomunicazioni, University of Trento (2003)Google Scholar
  4. 4.
    Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Noy, N.F., Doan, A., Halevy, A.Y.: Semantic integration. AI Magazine 26(1), 7–10 (2005)Google Scholar
  6. 6.
    Euzenat, J., Shvaiko, P.: Ontology matching. Springer, Heidelberg (2007)zbMATHGoogle Scholar
  7. 7.
    Bellahsene, Z., Bonifati, A., Rahm, E.: Schema Matching and Mapping. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  8. 8.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm and its application to schema matching. In: ICDE, pp. 117–128 (2002)Google Scholar
  9. 9.
    Alexe, B., Tan, W.C., Velegrakis, Y.: STBenchmark: towards a benchmark for mapping systems. Proceedings of the VLDB 1(1), 230–244 (2008)CrossRefGoogle Scholar
  10. 10.
    Do, H.-H., Melnik, S., Rahm, E.: Comparison of Schema Matching Evaluations. In: Chaudhri, A.B., Jeckle, M., Rahm, E., Unland, R. (eds.) NODe-WS 2002. LNCS, vol. 2593, pp. 221–237. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Fabien Duchateau
    • 1
  • Zohra Bellahsene
    • 2
  • Remi Coletta
    • 2
  1. 1.Norwegian University of Science and TechnologyTrondheimNorway
  2. 2.LIRMM - Université Montpellier 2MontpellierFrance

Personalised recommendations