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Uncertain schema matching 1 edition

Uncertain schema matching
Avigdor Gal

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Uncertain schema matching
Avigdor Gal

Published 2011 by Morgan & Claypool in San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) .
Written in English.

About the Book

Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. Schema matching is one of the basic operations required by the process of data and schema integration, and thus has a great effect on its outcomes, whether these involve targeted content delivery, view integration, database integration, query rewriting over heterogeneous sources, duplicate data elimination, or automatic streamlining of workflow activities that involve heterogeneous data sources. Although schema matching research has been ongoing for over 25 years, more recently a realization has emerged that schema matchers are inherently uncertain. Since 2003, work on the uncertainty in schema matching has picked up, along with research on uncertainty in other areas of data management. This lecture presents various aspects of uncertainty in schema matching within a single unified framework. We introduce basic formulations of uncertainty and provide several alternative representations of schema matching uncertainty. Then, we cover two common methods that have been proposed to deal with uncertainty in schema matching, namely ensembles, and top-K matchings, and analyze them in this context. We conclude with a set of real-world applications.

Table of Contents

1. Introduction
Schema matching in the data integration life cycle
Manuscript outline
2. Models of uncertainty
Probability theory
Fuzzy set theory
Triangular norms
Fuzzy aggregate operators
3. Modeling uncertain schema matching
Schema and attributes
Attribute correspondences and the similarity matrix
Schema matching
(Yet another) schema matcher classification
Model usage
Deep web information
Semantic matching
Holistic matching
Non-(1 : 1) cardinality constraints
Reasoning with uncertain schema matching
Reasoning using fuzzy set theory
Reasoning using probability theory
Assessing matching quality
The monotonicity principle
4. Schema matcher ensembles
The art of matcher ensembling
2LNB: a voting mechanism for ensembles
Constructing ensembles
SMB: schema matcher boosting
Ensemble construction with SMB: empirical analysis
5. Top-K schema matchings
Top-K schema matchings: definition
Algorithms for identifying top-K matchings
Finding the top-2 matchings using Abest
Finding the top-2 matchings using alternating paths
Finding the top-K matchings
Extending top-K identification to ensembles
The matrix-direct algorithm
Matrix-direct algorithm with bounding
Schema matching verification
Finding probabilistic attribute correspondences
6. Applications
Peer-to-peer data integration systems
Disaster data management
Web service discovery and composition
Communities of knowledge
7. Conclusions and future work
Author's biography.

Edition Notes

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 77-84).

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

Also available in print.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Synthesis lectures on data management -- # 13
Other Titles
Synthesis digital library of engineering and computer science.


Dewey Decimal Class
Library of Congress
QA76.9.D338 G258 2011

The Physical Object

[electronic resource] /

ID Numbers

Open Library
Internet Archive
9781608454341, 9781608454334


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