Usage-based schema matching

Research output: Chapter in Book/Report/Conference proceedingConference contribution

36 Citations (Scopus)

Abstract

Existing techniques for schema matching are classified as either schema-based, instance-based, or a combination of both. In this paper, we define a new class of techniques, called usage-based schema matching. The idea is to exploit information extracted from the query logs to find correspondences between attributes in the schemas to be matched. We propose methods to identify co-occurrence patterns between attributes in addition to other features such as their use in joins and with aggregate functions. Several scoring functions are considered to measure the similarity of the extracted features, and a genetic algorithm is employed to find the highest-score mappings between the two schemas. Our technique is suitable for matching schemas even when their attribute names are opaque. It can further be combined with existing techniques to obtain more accurate results. Our experimental study demonstrates the effectiveness of the proposed approach and the benefit of combining it with other existing approaches.

Original languageEnglish
Title of host publicationProceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08
Pages20-29
Number of pages10
DOIs
Publication statusPublished - 1 Oct 2008
Event2008 IEEE 24th International Conference on Data Engineering, ICDE'08 - Cancun, Mexico
Duration: 7 Apr 200812 Apr 2008

Publication series

NameProceedings - International Conference on Data Engineering
ISSN (Print)1084-4627

Other

Other2008 IEEE 24th International Conference on Data Engineering, ICDE'08
CountryMexico
CityCancun
Period7/4/0812/4/08

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Elmeleegy, H., Ouzzani, M., & Elmagarmid, A. (2008). Usage-based schema matching. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE'08 (pp. 20-29). [4497410] (Proceedings - International Conference on Data Engineering). https://doi.org/10.1109/ICDE.2008.4497410