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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2008.12
- 수록면
- 217 - 224 (8page)
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This paper provides a framework for semantic correspondence of heterogeneous databases using self- organizing map. It solves the problem of overlapping between different databases due to their different schemas. Clustering technique using self-organizing maps (SOM) is tested and evaluated to assess its performance when using different kinds of data. Preprocessing of database is performed prior to clustering using edit distance algorithm, principal component analysis (PCA), and normalization function to identify the features necessary for clustering.
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참고문헌 (16)
참고문헌 신청
C. Batini / 1986 / A comparative analysis of methodologies for database schema integration / ACM Computing Surveys :323~364
S. Ram / 2001 / Detecting both schema- level and instance- level correspondences for integration of E-catalogs / Proceedings of the Eleventh Annual Workshop on Information Technology and Systems (WITS’01), New Orleans, Louisiana, USA :193~198
T.W. Ryu / 2005 / A database clustering methodology and tool / Information Sciences 171:29~59
D. Heimbigner / 1985 / A federated architecture for information management / ACM Transactions on Office Information Systems 3(3):253~278
H. Zhao / 2007 / Combining Sschema and Instance Information for Integrating Hererogeneous Data Sources / Data and Knowledge Engineering Science Direct 61:281~303
S. Ram / 2001 / Detecting both schema- level and instance- level correspondences for integration of E-catalogs / Proceedings of the Eleventh Annual Workshop on Information Technology and Systems (WITS’01), New Orleans, Louisiana, USA :193~198
T.W. Ryu / 2005 / A database clustering methodology and tool / Information Sciences 171:29~59
D. Heimbigner / 1985 / A federated architecture for information management / ACM Transactions on Office Information Systems 3(3):253~278
H. Zhao / 2007 / Combining Sschema and Instance Information for Integrating Hererogeneous Data Sources / Data and Knowledge Engineering Science Direct 61:281~303
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UCI(KEPA) : I410-ECN-0101-2017-056-001394844