Simplifying Information Integration: Object-Based Flow-of-Mappings Framework for Integration

  • Bogdan Alexe
  • Michael Gubanov
  • Mauricio A. Hernández
  • Howard Ho
  • Jen-Wei Huang
  • Yannis Katsis
  • Lucian Popa
  • Barna Saha
  • Ioana Stanoi
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 27)


The Clio project at IBM Almaden investigates foundational aspects of data transformation, with particular emphasis on the design and execution of schema mappings. We now use Clio as part of a broader data-flow framework in which mappings are just one component. These data-flows express complex transformations between several source and target schemas and require multiple mappings to be specified. This paper describes research issues we have encountered as we try to create and run these mapping-based data-flows. In particular, we describe how we use Unified Famous Objects (UFOs), a schema abstraction similar to business objects, as our data model, how we reason about flows of mappings over UFOs, and how we create and deploy transformations into different run-time engines.


Schema Mappings Schema Decomposition Mapping Composition Mapping Merge Data Flows 


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  1. 1.
    Bergamaschi, S., Castano, S., Vincini, M., Beneventano, D.: Semantic integration of heterogeneous information sources. Data Knowl. Eng. 36(3), 215–249 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bernstein, P.A., Green, T.J., Melnik, S., Nash, A.: Implementing Mapping Composition. In: Proceedings of VLDB, pp. 55–66 (2006)Google Scholar
  3. 3.
    Dessloch, S., Hernández, M.A., Wisnesky, R., Radwan, A., Zhou, J.: Orchid: Integrating Schema Mapping and ETL. In: ICDE, pp. 1307–1316 (2008)Google Scholar
  4. 4.
    Do, H.-H., Rahm, E.: Coma: a system for flexible combination of schema matching approaches. In: VLDB 2002, pp. 610–621 (2002)Google Scholar
  5. 5.
    Doan, A., Domingos, P., Halevy, A.Y.: Reconciling schemas of disparate data sources: a machine-learning approach. In: SIGMOD 2001, pp. 509–520 (2001)Google Scholar
  6. 6.
    Doan, A., Madhavan, J., Domingos, P., Halevy, A.: Learning to map between ontologies on the semantic web. In: WWW 2002, pp. 662–673 (2002)Google Scholar
  7. 7.
    Fagin, R., Kolaitis, P., Popa, L., Tan, W.-C.: Composing Schema Mappings: Second-Order Dependencies to the Rescue. In: PODS, pp. 83–94 (2004)Google Scholar
  8. 8.
    Fagin, R., Kolaitis, P.G., Miller, R.J., Popa, L.: Data exchange: semantics and query answering. Theoretical Computer Science 336(1), 89–124 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Fuxman, A., Hernández, M.A., Ho, H., Miller, R.J., Papotti, P., Popa, L.: Nested Mappings: Schema Mapping Reloaded. In: Proceedings of VLDB, pp. 67–78 (2006)Google Scholar
  10. 10.
    Li, W.-S., Clifton, C.: Semint: a tool for identifying attribute correspondences in heterogeneous databases using neural networks. Data Knowl. Eng. 33(1), 49–84 (2000)CrossRefzbMATHGoogle Scholar
  11. 11.
    Madhavan, J., Bernstein, P.A., Doan, A., Halevy, A.: Corpus-based schema matching. In: ICDE 2005, pp. 57–68 (2005)Google Scholar
  12. 12.
    Madhavan, J., Bernstein, P.A., Rahm, E.: Generic schema matching with cupid. In: VLDB 2001, pp. 49–58 (2001)Google Scholar
  13. 13.
    Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: A versatile graph matching algorithm. In: ICDE 2002, pp. 117–128 (2002)Google Scholar
  14. 14.
    Miller, R.J., Haas, L.M., Hernández, M.A.: Schema mapping as query discovery. In: VLDB 2000, pp. 77–88 (2000)Google Scholar
  15. 15.
    Milo, T., Zohar, S.: Using schema matching to simplify heterogeneous data translation. In: VLDB 1998, pp. 122–133 (1998)Google Scholar
  16. 16.
    Popa, L., Velegrakis, Y., Hernández, M.A., Miller, R.J., Fagin, R.: Translating web data. In: VLDB 2002, pp. 598–609 (2002)Google Scholar
  17. 17.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. The VLDB Journal 10(4), 334–350 (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Shvaiko, P., Euzenat, J.: A survey of schema-based matching approaches. In: Spaccapietra, S. (ed.) Journal on Data Semantics IV. LNCS, vol. 3730, pp. 146–171. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Bogdan Alexe
    • 1
  • Michael Gubanov
    • 2
  • Mauricio A. Hernández
    • 3
  • Howard Ho
    • 3
  • Jen-Wei Huang
    • 4
  • Yannis Katsis
    • 5
  • Lucian Popa
    • 3
  • Barna Saha
    • 6
  • Ioana Stanoi
    • 3
  1. 1.University of California, Santa CruzUSA
  2. 2.University of WashingtonUSA
  3. 3.IBM Almaden Research CenterUSA
  4. 4.National Taiwan UniversityTaiwan
  5. 5.University of California, San DiegoUSA
  6. 6.University of MarylandUSA

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