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Clustering Similar Schema Elements Across Heterogeneous Databases: A First Step in Database Integration

Clustering Similar Schema Elements Across Heterogeneous Databases: A First Step in Database Integration

Huimin Zhao, Sudha Ram
Copyright: © 2006 |Pages: 22
ISBN13: 9781591409359|ISBN10: 1591409357|ISBN13 Softcover: 9781591409366|EISBN13: 9781591409373
DOI: 10.4018/978-1-59140-935-9.ch013
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MLA

Zhao, Huimin, and Sudha Ram. "Clustering Similar Schema Elements Across Heterogeneous Databases: A First Step in Database Integration." Advanced Topics in Database Research, Volume 5, edited by Keng Siau, IGI Global, 2006, pp. 227-248. https://doi.org/10.4018/978-1-59140-935-9.ch013

APA

Zhao, H. & Ram, S. (2006). Clustering Similar Schema Elements Across Heterogeneous Databases: A First Step in Database Integration. In K. Siau (Ed.), Advanced Topics in Database Research, Volume 5 (pp. 227-248). IGI Global. https://doi.org/10.4018/978-1-59140-935-9.ch013

Chicago

Zhao, Huimin, and Sudha Ram. "Clustering Similar Schema Elements Across Heterogeneous Databases: A First Step in Database Integration." In Advanced Topics in Database Research, Volume 5, edited by Keng Siau, 227-248. Hershey, PA: IGI Global, 2006. https://doi.org/10.4018/978-1-59140-935-9.ch013

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Abstract

Interschema relationship identification (IRI), that is, determining the relationships among schema elements in heterogeneous data sources, is an important first step in integrating the data sources. This chapter proposes a cluster analysis-based approach to semi-automating the IRI process, which is typically very time-consuming and requires extensive human interaction. We apply multiple clustering techniques, including K-means, hierarchical clustering, and self-organizing map (SOM) neural network, to identify similar schema elements from heterogeneous data sources, based on multiple types of features, such as naming similarity, document similarity, schema specification, data patterns, and usage patterns. We describe an SOM prototype we have developed that provides users with a visualization tool for displaying clustering results and for incremental evaluation of potentially similar elements. We also report on some empirical results demonstrating the utility of the proposed approach.

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