000 03448nam a22004935i 4500
001 978-3-031-01845-9
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007 cr nn 008mamaa
008 220601s2011 sz | s |||| 0|eng d
020 _a9783031018459
_9978-3-031-01845-9
024 7 _a10.1007/978-3-031-01845-9
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM043000
_2bisacsh
072 7 _aUKN
_2thema
082 0 4 _a004.6
_223
100 1 _aGal, Avigdor.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_980253
245 1 0 _aUncertain Schema Matching
_h[electronic resource] /
_cby Avigdor Gal.
250 _a1st ed. 2011.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2011.
300 _aXII, 85 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Data Management,
_x2153-5426
505 0 _aIntroduction -- Models of Uncertainty -- Modeling Uncertain Schema Matching -- Schema Matcher Ensembles -- Top-K Schema Matchings -- Applications -- Conclusions and Future Work.
520 _aSchema 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.
650 0 _aComputer networks .
_931572
650 0 _aData structures (Computer science).
_98188
650 0 _aInformation theory.
_914256
650 1 4 _aComputer Communication Networks.
_980254
650 2 4 _aData Structures and Information Theory.
_931923
710 2 _aSpringerLink (Online service)
_980255
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031007170
776 0 8 _iPrinted edition:
_z9783031029738
830 0 _aSynthesis Lectures on Data Management,
_x2153-5426
_980256
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01845-9
912 _aZDB-2-SXSC
942 _cEBK
999 _c84926
_d84926