Clustering similar schema elements across heterogeneous databases: A first step in database integration

Huimin Zhao, Sudha Ram

Research output: Chapter in Book/Report/Conference proceedingChapter

3 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdvanced Topics in Database Research
PublisherIGI Global
Pages227-248
Number of pages22
Volume5
ISBN (Print)9781591409359
DOIs
StatePublished - Dec 1 2006

ASJC Scopus subject areas

  • Social Sciences(all)

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