Matching Schemas From Disparate Data Sources

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Conclusion• Schema matching is the problem of identifyingcorresponding elements in different schemas• A challenging problem• Very active area of research with variousmarket applications• Working prototype• Different matchers, combine results usingsome strategies
Future Work• More work on the prototype– Study performance (metrics) of differentmatchers (what works best on which data)– Better filtering of matching results– Large test data– Opaque column matching!– Learning over time to better match, setthresholds
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 and 12: • Training/domain expert:- Ask us
Page 13 and 14: • Fragment-oriented:- Decompose X
Page 15 and 16: Design• A collection of matchers
Page 17 and 18: • Name Matcher (Exact, Levenshtei
Page 30: Thanks! ☺Any Q?

Matching Schemas From Disparate Data Sources

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