Abstract
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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Bilke, A., Naumann, F.: Schema matching using duplicates. In: Proceeding of the 21st International Conference on Data Engineering, pp. 69–80 (2005)
Zhao, H., Ram, S.: Clustering schema elements for semantic integration of heterogeneous data source. Journal of Database Management 15, 88–106 (2004)
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)
Doan, A., Domingos, P., Halevy, A.: Reconciling Schemas of Disparate Data Sources: A Machine-Learning approach. SIGMOD, 509–520 (2001)
Dhamankar, R., Lee, Y., Doan, A.: Imap: discovering complex semantic matches between database schemas. SIGMOD, 13–18 (2004)
Madhavan, J., Bernstein, P.A., Rahm, E.: Generic Schema Matching with Cupid. VLDB, 49–58 (2001)
Melnik, S., Molina, H.G., Rahm, E.: Similarity Flooding: A versatile graph matching algorithm and its application to schema matching. ICDE, 117–128 (2002)
Li, G., Du, X., Du, J.: A structure matching method based on partial funtional depencies. Chinese Journal of Computers 33, 240–250 (2010)
Do, H.H., Rahm, E.: COMA-A system for flexible combination of schema matching approaches. VLDB, 610–621 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag GmbH Berlin Heidelberg
About this chapter
Cite this chapter
Liu, G., Huang, S., Cheng, Y. (2012). Research on Semantic Integration across Heterogeneous Data Sources in Grid. In: Sambath, S., Zhu, E. (eds) Frontiers in Computer Education. Advances in Intelligent and Soft Computing, vol 133. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27552-4_56
Download citation
DOI: https://doi.org/10.1007/978-3-642-27552-4_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27551-7
Online ISBN: 978-3-642-27552-4
eBook Packages: EngineeringEngineering (R0)