Instance-based attribute identification in database integration

Instance-based attribute identification in database integration Most research on attribute identification in database integration has focused on integrating attributes using schema and summary information derived from the attribute values. No research has attempted to fully explore the use of attribute values to perform attribute identification. We propose an attribute identification method that employs schema and summary instance information as well as properties of attributes derived from their instances. Unlike other attribute identification methods that match only single attributes, our method matches attribute groups for integration. Because our attribute identification method fully explores data instances, it can identify corresponding attributes to be integrated even when schema information is misleading. Three experiments were performed to validate our attribute identification method. In the first experiment, the heuristic rules derived for attribute classification were evaluated on 119 attributes from nine public domain data sets. The second was a controlled experiment validating the robustness of the proposed attribute identification method by introducing erroneous data. The third experiment evaluated the proposed attribute identification method on five data sets extracted from online music stores. The results demonstrated the viability of the proposed method. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Instance-based attribute identification in database integration

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Publisher
Springer-Verlag
Copyright
Copyright © 2003 by Springer-Verlag
Subject
ComputerScience
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-003-0088-y
Publisher site
See Article on Publisher Site

Abstract

Most research on attribute identification in database integration has focused on integrating attributes using schema and summary information derived from the attribute values. No research has attempted to fully explore the use of attribute values to perform attribute identification. We propose an attribute identification method that employs schema and summary instance information as well as properties of attributes derived from their instances. Unlike other attribute identification methods that match only single attributes, our method matches attribute groups for integration. Because our attribute identification method fully explores data instances, it can identify corresponding attributes to be integrated even when schema information is misleading. Three experiments were performed to validate our attribute identification method. In the first experiment, the heuristic rules derived for attribute classification were evaluated on 119 attributes from nine public domain data sets. The second was a controlled experiment validating the robustness of the proposed attribute identification method by introducing erroneous data. The third experiment evaluated the proposed attribute identification method on five data sets extracted from online music stores. The results demonstrated the viability of the proposed method.

Journal

The VLDB JournalSpringer Journals

Published: Oct 1, 2003

References

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