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Uncertain Schema Matching (Paperback)
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Uncertain Schema Matching (Paperback)
Series: Synthesis Lectures on Data Management
Expected to ship within 10 - 15 working days
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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: Introduction /
Models of Uncertainty / Modeling Uncertain Schema Matching / Schema
Matcher Ensembles / Top-K Schema Matchings / Applications /
Conclusions and Future Work
General
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