Matching Schemas From Disparate Data Sources

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• Information theory-based:– Column names can be opaque!– Construct dependency graph using mutualinformation between columns– Reduces to a graph matching problem[Kang and Naughton SIGMOD03]• Rule-based (iMAP):– Search space of all possible matches“efficiently”– Apply rules that reflect domain constraints[Dhamanker, Lee, Doan et al SIGMOD04]
• Fragment-oriented:– Decompose XML trees into much smallersub-trees and match from bottom up[Rahm et al ACM SIGMOD Records04]• <strong>Matching</strong> different designs -- values asattribute names:– Extends SQL to promote and demoteattribute names[Wyss, Fletcher et al. ACM trans on DB05 ]
Page 1 and 2: Matching SchemasFrom DisparateData
Page 3 and 4: What is Semantic Matching?• Mappi
Page 5 and 6: Schema Sfantastic house 250,000 1
Page 7 and 8: Manual integration is notpractical
Page 9 and 10: • Matching Accuracy!Key Challenge
Page 11: • Training/domain expert:- Ask us
Page 15 and 16: Design• A collection of matchers
Page 17 and 18: • Name Matcher (Exact, Levenshtei
Page 28 and 29: Conclusion• Schema matching is th
Page 30: Thanks! ☺Any Q?

Matching Schemas From Disparate Data Sources

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