Research on Semantic Integration across Heterogeneous Data Sources in Grid

  • Guofeng LiuEmail author
  • Shaobin Huang
  • Yuan Cheng
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 133)


Grid technology is a kind of important network information technology grows up in recent years, which can settle the problems of fully sharing and interactive applying among different kinds of resources (such as computing resources, storage resources etc.) distributing in the wide area. This paper focuses on the difficulties of semantic integration across heterogeneous data source in grid. For the existing automatic/semi-automatic schema matching algorithm, it analyzes the advantages and disadvantages and presents a generic schema matching model that full use of the schema and instance information in the schema.


Semantic Integration Schema Matching Text Classification Grid 


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  1. 1.
    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
  2. 2.
    Pei, J., Hong, J., Bell, D.A.: A novel clustering-based approach to schema matching. In: Yakhno, T., Neuhold, E.J. (eds.) ADVIS 2006. LNCS, vol. 4243, pp. 60–69. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 3.
    Berlin, J., Motro, A.: Autoplex: Automated discovery of content for virtual databases. In: Batini, C., Giunchiglia, F., Giorgini, P., Mecella, M. (eds.) CoopIS 2001. LNCS, vol. 2172, pp. 108–122. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Bilke, A., Naumann, F.: Schema matching using duplicates. In: Proceeding of the 21st International Conference on Data Engineering, pp. 69–80 (2005)Google Scholar
  5. 5.
    Zhao, H., Ram, S.: Clustering schema elements for semantic integration of heterogeneous data source. Journal of Database Management 15, 88–106 (2004)CrossRefGoogle Scholar
  6. 6.
    Li, W.-S., Clifton, C.: SEMINT: A Tool for Identifying Attribute Correspondences in Heterogeneous Database Using Neural Networks. Data and Knowledge Engineering 33, 49–84 (2000)zbMATHCrossRefGoogle Scholar
  7. 7.
    Doan, A., Domingos, P., Halevy, A.: Reconciling Schemas of Disparate Data Sources: A Machine-Learning approach. SIGMOD, 509–520 (2001)Google Scholar
  8. 8.
    Dhamankar, R., Lee, Y., Doan, A.: Imap: discovering complex semantic matches between database schemas. SIGMOD, 13–18 (2004)Google Scholar
  9. 9.
    Madhavan, J., Bernstein, P.A., Rahm, E.: Generic Schema Matching with Cupid. VLDB, 49–58 (2001)Google Scholar
  10. 10.
    Melnik, S., Molina, H.G., Rahm, E.: Similarity Flooding: A versatile graph matching algorithm and its application to schema matching. ICDE, 117–128 (2002)Google Scholar
  11. 11.
    Li, G., Du, X., Du, J.: A structure matching method based on partial funtional depencies. Chinese Journal of Computers 33, 240–250 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Do, H.H., Rahm, E.: COMA-A system for flexible combination of schema matching approaches. VLDB, 610–621 (2002)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyHarbin Engineering UniversityHarbinChina

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