Uncertain schema matching
By: Avigdor Gal
Series: Synthesis lectures on data management -- # 13
Publisher: Morgan & Claypool; Morgan & Claypool Publishers
Published: 2011
Place of publication: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA)
Language: eng
Format: [electronic resource] /
Number of pages: 85
ISBN-13: 9781608454341; 9781608454334
Subjects: Statistical matching; Uncertainty (Information theory); Data integration (Computer science)
Other titles: Synthesis digital library of engineering and computer science.
Description: 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.
Notes: Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 77-84).

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