Generic Schema Mappings

  • David Kensche
  • Christoph Quix
  • Yong Li
  • Matthias Jarke
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4801)


Schema mappings come in different flavors: simple correspondences are produced by schema matchers, intensional mappings are used for schema integration. However, the execution of mappings requires a formalization based on the extensional semantics of models. This problem is aggravated if multiple metamodels are involved. In this paper we present extensional mappings, that are based on second order tuple generating dependencies, between models in our Generic Role-based Metamodel GeRoMe. By using a generic metamodel, our mappings support data translation between heterogeneous metamodels. Our mapping representation provides grouping functionalities that allow for complete restructuring of data, which is necessary for handling nested data structures such as XML and object oriented models. Furthermore, we present an algorithm for mapping composition and optimization of the composition result. To verify the genericness, correctness, and composability of our approach we implemented a data translation tool and mapping export for several data manipulation languages.


Modeling Language Relational Schema Mapping Composition Composition Algorithm Mapping Execution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Atzeni, P., Cappellari, P., Bernstein, P.A.: Model-independent schema and data translation. In EDBT. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Boehm, K., Kemper, A., Grust, T., Boehm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 368–385. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Bernstein, P.A., Green, T.J., Melnik, S., Nash, A.: Implementing mapping composition. In: Proc. VLDB 2006, Seoul, pp. 55–66 (2006)Google Scholar
  3. 3.
    Bernstein, P.A., Halevy, A.Y., Pottinger, R.: A vision for management of complex models. SIGMOD Record 29(4), 55–63 (2000)CrossRefGoogle Scholar
  4. 4.
    Fagin, R., Kolaitis, P.G., Popa, L., Tan, W.C.: Composing schema mappings: Second-order dependencies to the rescue. ACM Trans. Database Syst. 30(4), 994–1055 (2005)CrossRefGoogle Scholar
  5. 5.
    Fuxman, A., Hernández, M.A., Ho, C.T.H., Miller, R.J., Papotti, P., Popa, L.: Nested mappings: Schema mapping reloaded. In: Proc. VLDB 2006, Seoul, pp. 67–78 (2006)Google Scholar
  6. 6.
    Hernández, M.A., Miller, R.J., Haas, L.M.: Clio: A semi-automatic tool for schema mapping. In: Proc. ACM SIGMOD, p. 607. ACM Press, New York (2001)Google Scholar
  7. 7.
    Kensche, D., Quix, C., Chatti, M.A., Jarke, M.: GeRoMe: A generic role based metamodel for model management. Journal on Data Semantics VIII, 82–117 (2007)zbMATHGoogle Scholar
  8. 8.
    Kensche, D., Quix, C., Li, X., Li, Y.: GeRoMeSuite: A system for holistic generic model management. In: Proc. 33rd Int. Conf. on Very Large Data Bases (to appear 2007)Google Scholar
  9. 9.
    Lenzerini, M.: Data integration: A theoretical perspective. In: PODS, pp. 233–246 (2002)Google Scholar
  10. 10.
    Li, Y.: Composition of mappings for a generic meta model. Master’s thesis, RWTH Aachen University (2007)Google Scholar
  11. 11.
    Madhavan, J., Halevy, A.Y.: Composing mappings among data sources. In: Proc. VLDB, pp. 572–583. Morgan Kaufmann, San Francisco (2003)Google Scholar
  12. 12.
    Melnik, S., Bernstein, P.A., Halevy, A.Y., Rahm, E.: Supporting executable mappings in model management. In: Proc. SIGMOD Conf, pp. 167–178. ACM Press, New York (2005)Google Scholar
  13. 13.
    Melnik, S., Rahm, E., Bernstein, P.A.: Rondo: A programming platform for generic model management. In: Proc. SIGMOD, pp. 193–204. ACM Press, New York (2003)Google Scholar
  14. 14.
    Nash, A., Bernstein, P.A., Melnik, S.: Composition of mappings given by embedded dependencies. In: Li, C. (ed.) PODS, pp. 172–183. ACM Press, New York (2005)Google Scholar
  15. 15.
    Popa, L., Tannen, V.: An equational chase for path-conjunctive queries, constraints, and views. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 39–57. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  16. 16.
    Quix, C., Kensche, D., Li, X.: Generic schema merging. In: Krogstie, J., Opdahl, A., Sindre, G. (eds.) CAiSE 2007. LNCS, pp. 127–141. Springer, Heidelberg (2007)Google Scholar
  17. 17.
    Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB Journal 10(4), 334–350 (2001)CrossRefzbMATHGoogle Scholar
  18. 18.
    Robertson, G.G., Czerwinski, M.P., Churchill, J.E.: Visualization of mappings between schemas. In: Proc. SIGCHI, pp. 431–439 (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • David Kensche
    • 1
  • Christoph Quix
    • 1
  • Yong Li
    • 1
  • Matthias Jarke
    • 1
  1. 1.RWTH Aachen University, Informatik 5 (Information Systems), 52056 AachenGermany

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