A Scalable Algorithm for One-to-One, Onto, and Partial Schema Matching with Uninterpreted Column Names and Column Values
In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focuse...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rabinovich, Boris [verfasserIn] Last, Mark [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Umfang: |
1 Online-Ressource |
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| Übergeordnetes Werk: |
Enthalten in: Journal of database management - Hershey, Pa : IGI Global, 2000, 25(2014), 4, Seite 1-16 |
|---|---|
| Übergeordnetes Werk: |
volume:25 ; year:2014 ; number:4 ; pages:1-16 |
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| DOI / URN: |
10.4018/JDM.2014100101 |
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| 520 | |a In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques | ||
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10.4018/JDM.2014100101 doi (DE-627)NLEJ251841766 (VZGNL)10.4018/JDM.2014100101 DE-627 ger DE-627 rakwb eng Rabinovich, Boris verfasserin aut A Scalable Algorithm for One-to-One, Onto, and Partial Schema Matching with Uninterpreted Column Names and Column Values 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques Attribute Dependency Data Mining Data Modeling Database Semantics Graph Matching Schema Matching Last, Mark verfasserin aut Enthalten in Journal of database management Hershey, Pa : IGI Global, 2000 25(2014), 4, Seite 1-16 Online-Ressource (DE-627)NLEJ24441971X (DE-600)2070075-1 1533-8010 nnns volume:25 year:2014 number:4 pages:1-16 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 25 2014 4 1-16 |
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10.4018/JDM.2014100101 doi (DE-627)NLEJ251841766 (VZGNL)10.4018/JDM.2014100101 DE-627 ger DE-627 rakwb eng Rabinovich, Boris verfasserin aut A Scalable Algorithm for One-to-One, Onto, and Partial Schema Matching with Uninterpreted Column Names and Column Values 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques Attribute Dependency Data Mining Data Modeling Database Semantics Graph Matching Schema Matching Last, Mark verfasserin aut Enthalten in Journal of database management Hershey, Pa : IGI Global, 2000 25(2014), 4, Seite 1-16 Online-Ressource (DE-627)NLEJ24441971X (DE-600)2070075-1 1533-8010 nnns volume:25 year:2014 number:4 pages:1-16 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 25 2014 4 1-16 |
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10.4018/JDM.2014100101 doi (DE-627)NLEJ251841766 (VZGNL)10.4018/JDM.2014100101 DE-627 ger DE-627 rakwb eng Rabinovich, Boris verfasserin aut A Scalable Algorithm for One-to-One, Onto, and Partial Schema Matching with Uninterpreted Column Names and Column Values 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques Attribute Dependency Data Mining Data Modeling Database Semantics Graph Matching Schema Matching Last, Mark verfasserin aut Enthalten in Journal of database management Hershey, Pa : IGI Global, 2000 25(2014), 4, Seite 1-16 Online-Ressource (DE-627)NLEJ24441971X (DE-600)2070075-1 1533-8010 nnns volume:25 year:2014 number:4 pages:1-16 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 25 2014 4 1-16 |
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10.4018/JDM.2014100101 doi (DE-627)NLEJ251841766 (VZGNL)10.4018/JDM.2014100101 DE-627 ger DE-627 rakwb eng Rabinovich, Boris verfasserin aut A Scalable Algorithm for One-to-One, Onto, and Partial Schema Matching with Uninterpreted Column Names and Column Values 2014 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques Attribute Dependency Data Mining Data Modeling Database Semantics Graph Matching Schema Matching Last, Mark verfasserin aut Enthalten in Journal of database management Hershey, Pa : IGI Global, 2000 25(2014), 4, Seite 1-16 Online-Ressource (DE-627)NLEJ24441971X (DE-600)2070075-1 1533-8010 nnns volume:25 year:2014 number:4 pages:1-16 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 25 2014 4 1-16 |
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In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques |
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In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques |
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In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">NLEJ251841766</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231205144037.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231128s2014 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.4018/JDM.2014100101</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)NLEJ251841766</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(VZGNL)10.4018/JDM.2014100101</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Rabinovich, Boris</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Scalable Algorithm for One-to-One, Onto, and Partial Schema Matching with Uninterpreted Column Names and Column Values</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">In this paper, the authors propose a five-step approach to the problem of identifying semantic correspondences between attributes of two database schemas. It is one of the key challenges in many database applications such as data integration and data warehousing. The authors' research is focused on uninterpreted schema matching, where the column names and column values are uninterpreted or unreliable. The approach implements Bayesian networks, Pearson's correlation and mutual information to identify inter-attribute dependencies. Additionally, the authors propose an extension to their algorithm that allows the user to manually enter the known mappings to improve the automated matching results. The five-step approach also allows data privacy preservation. The authors' evaluation experiments show that the proposed approach enhances the current set of schema matching techniques</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Attribute Dependency</subfield><subfield code="a">Data Mining</subfield><subfield code="a">Data Modeling</subfield><subfield code="a">Database Semantics</subfield><subfield code="a">Graph Matching</subfield><subfield code="a">Schema Matching</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Last, Mark</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of database management</subfield><subfield code="d">Hershey, Pa : IGI Global, 2000</subfield><subfield code="g">25(2014), 4, Seite 1-16</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)NLEJ24441971X</subfield><subfield code="w">(DE-600)2070075-1</subfield><subfield code="x">1533-8010</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:25</subfield><subfield code="g">year:2014</subfield><subfield code="g">number:4</subfield><subfield code="g">pages:1-16</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101</subfield><subfield code="m">X:IGIG</subfield><subfield code="x">Verlag</subfield><subfield code="z">Deutschlandweit zugänglich</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/JDM.2014100101&buylink=true</subfield><subfield code="3">Abstract</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-1-GIS</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_NL_ARTICLE</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">25</subfield><subfield code="j">2014</subfield><subfield code="e">4</subfield><subfield code="h">1-16</subfield></datafield></record></collection>
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