Generic Conceptual Framework for Handling Schema Diversity during Source Integration

Semantic Database Case Study
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 186)

Abstract

DataBase Integration Systems (DIS) aim at providing a unified view of data stored in different heterogeneous local sources through a global schema. The schemas of global and local sources are represented by a − prioriknown logical representations. This makes the potential deployment model of the DIS, like for Data Warehouse (DW) systems, rigid and inflexible. To overcome these limitations, we claim that the integration process should be completely performed at the conceptuallevel independently of any implementation constraint. After studying existing database (DB) integration systems, we propose through this paper a generic conceptual framework for DB schema integration. This framework is generic as it subsumes most important DB integration systems studied. It is defined based on the description logic formalism. We then instantiate it through a case study considering a set of Oracle semantic DB, that are DB storing their own conceptual model, generated using Lehigh University BenchMark (LUBM). These DB participate in the construction of a semantic DW.

Keywords

DataBase Integration Description Logic Semantic Databases 

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References

  1. 1.
    Bellatreche, L., Nguyen Xuan, D., Pierra, G., Dehainsala, H.: Contribution of ontology-based data modeling to automatic integration of electronic catalogues within engineering databases. Computers in Industry Journal Elsevier 57(8-9), 711–724 (2006)CrossRefGoogle Scholar
  2. 2.
    Brockmans, S., Haase, P., Serafini, L., Stuckenschmidt, H.: Formal and Conceptual Comparison of Ontology Mapping Languages. In: Stuckenschmidt, H., Parent, C., Spaccapietra, S. (eds.) Modular Ontologies. LNCS, vol. 5445, pp. 267–291. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Calvanese, D., De Giacomo, G., Lembo, D., Lenzerini, M., Poggi, A., Rosati, R., Ruzzi, M.: Data Integration through \({\textit{DL-Lite}_{\mathcal A}}\) Ontologies. In: Schewe, K.-D., Thalheim, B. (eds.) SDKB 2008. LNCS, vol. 4925, pp. 26–47. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Calvanese, D., Giacomo, G., Lembo, D., Lenzerini Rosati, R., Ruzzi, M.: Using owl in data integration. In: Semantic Web Information Management, pp. 397–424 (2009)Google Scholar
  5. 5.
    Calvanese, D., Giacomo, G., Lenzerini, M.: A framework for ontology integration. In: SWWS, pp. 303–316 (2001)Google Scholar
  6. 6.
    Calvanese, D., Giacomo, G., Lenzerini, M., Nardi, D., Rosati, R.: Data integration in data warehousing. Int. J. Cooperative Inf. Syst. 10(3), 237–271 (2001)CrossRefGoogle Scholar
  7. 7.
    Calvanese, D., Lenzerini, M., Nardi, D.: Description logics for conceptual data modeling. In: Logics for Databases and Information Systems, pp. 229–263 (1998)Google Scholar
  8. 8.
    Catarci, T., Lenzerini, M.: Representing and using interschema knowledge in cooperative information systems. Int. J. Cooperative Inf. Syst. 2(4), 375–398 (1993)CrossRefGoogle Scholar
  9. 9.
    Dehainsala, H., Pierra, G., Bellatreche, L.: OntoDB: An Ontology-Based Database for Data Intensive Applications. In: Kotagiri, R., Radha Krishna, P., Mohania, M., Nantajeewarawat, E. (eds.) DASFAA 2007. LNCS, vol. 4443, pp. 497–508. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Goasdoué, F., Karanasos, K., Leblay, J., Manolescu, I.: View selection in semantic web databases. PVLDB 5(2), 97–108 (2011)Google Scholar
  11. 11.
    Guo, Y., Pan, Z., Heflin, J.: Lubm: A benchmark for owl knowledge base systems. Journal of Web Semantics, 158–182 (2005)Google Scholar
  12. 12.
    Haase, P., Motik, B.: A mapping system for the integration of owl-dl ontologies. In: IHIS, pp. 9–16 (2005)Google Scholar
  13. 13.
    Halevy, A.Y., Ashish, N., Bitton, D., Carey, M.J., Draper, D., Pollock, J., Rosenthal, A., Sikka, V.: Enterprise information integration: successes, challenges and controversies. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 778–787 (2005)Google Scholar
  14. 14.
    Khouri, S., Bellatreche, L.: A methodology and tool for conceptual designing a data warehouse from ontology-based sources. In: DOLAP 2010, pp. 19–24 (2010)Google Scholar
  15. 15.
    Lenzerini, M.: Data integration: A theoretical perspective. In: PODS, pp. 233–246 (2002)Google Scholar
  16. 16.
    Lu, J., Ma, L., Zhang, L., Brunner, J.S., Wang, C., Pan, Y., Yu, Y.: Sor: A practical system for ontology storage, reasoning and search. In: VLDB, pp. 1402–1405 (2007)Google Scholar
  17. 17.
    Skoutas, D., Simitsis, A.: Ontology-based conceptual design of etl processes for both structured and semi-structured data. Int. J. Semantic Web Inf. Syst. 3(4), 1–24 (2007)CrossRefGoogle Scholar
  18. 18.
    Wu, Z., Eadon, G., Das, S., Chong, E., Kolovski, V., Annamalai, M., Srinivasan, J.: Implementing an inference engine for rdfs/owl constructs and user-defined rules in oracle. In: ICDE, pp. 1239–1248 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Selma Khouri
    • 1
    • 2
  • Ladjel Bellatreche
    • 1
  • Nabila Berkani
    • 2
  1. 1.LIAS/ISAE-ENSMA Poitiers UniversityPoitiersFrance
  2. 2.National High School for Computer Science (ESI)AlgiersAlgeria

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