Encyclopedia of Database Systems

Living Edition
| Editors: Ling Liu, M. Tamer Özsu

Interoperability in Data Warehouses

  • Riccardo TorloneEmail author
Living reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7993-3_207-2



The term refers to the ability of combining the content of two or more heterogeneous data warehouses, for the purpose of cross-analysis. This need emerges in a variety of practical situations. For instance, when different designers of a large company develop their data marts independently, or when different organizations involved in the same project need to integrate their data warehouses.

Data Warehouse interoperability is a special case of the general problem of database integration, but it can be tackled in a more systematic way because data warehouses are structured in a rather uniform way, along the widely accepted concepts of dimension and fact. As it happens in the general case, different degrees of interoperability can be pursued by adopting standards and/or by applying reconciliation techniques, likely specific for this context.

The problem is becoming increasingly relevant with the spreading of federated architectures....


Resource Description Framework Data Cube Object Management Group Link Open Data Structural Conflict 
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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Recommended Reading

  1. 1.
    Abelló A, Darmont J, Etcheverry L, Golfarelli M, Mazón J-N, Naumann F, Pedersen TB, Rizzi S, Trujillo J, Vassiliadis P, Vossen G. Fusion cubes: towards self-service business intelligence. J Data Warehous Min. 2013;9(2):66–88.CrossRefGoogle Scholar
  2. 2.
    Abelló A, Romero O, Pedersen TB, Llavori RB, Nebot V, Aramburu Cabo MJ, Simitsis A. Using semantic web technologies for exploratory OLAP: a survey. IEEE Trans Knowl Data Eng. 2015;27(2):571–88.CrossRefGoogle Scholar
  3. 3.
    Banek M, Vrdoljak B, Min Tjoa A, Skocir Z. Automating the schema matching process for heterogeneous data warehouses. In: Proceedings of 9th International Conference on Data Warehousing and Knowledge Discovery; 2007. p. 45–54.Google Scholar
  4. 4.
    Bergamaschi S, Olaru M. O., Sorrentino S, Vincini M. Dimension matching in Peer-to-Peer Data Warehousing. In: Proceedings of 8th International Conference on Decision Support Systems; 2012. p. 149–60.Google Scholar
  5. 5.
    Berger S, Schrefl M. Analysing multi-dimensional data across autonomous data warehouses. In: Proceedings of 8th International Conference on Data Warehousing and Knowledge Discovery; 2006. p. 120–33.Google Scholar
  6. 6.
    Berger S, Schrefl M. FedDW global schema architect: UML-based design tool for the integration of data mart schemas. In: Proceedings of 15th International Workshop on Data warehousing and OLAP; 2012. p. 33–40.Google Scholar
  7. 7.
    Cabibbo L, Torlone R. On the integration of autonomous data marts. In: Proceedings of 16th International Conference on Scientific and Statistical Database Management; 2004. p. 223–34.Google Scholar
  8. 8.
    Cabibbo L, Torlone R. Integrating heterogeneous multidimensional databases. In: Proceedings of 17th International Conference on Scientific and Statistical Database Management; 2005. p. 205–14.Google Scholar
  9. 9.
    Cabibbo L, Panella I. Torlone R. DaWaII: a tool for the integration of autonomous data marts. In: Proceedings of 22nd International Conference on Data Engineering, Demo session; 2006.Google Scholar
  10. 10.
    Etcheverry L, Vaisman A, Zimányi E. Modeling and querying data warehouses on the semantic web using QB4OLAP. In: Proceedings of 16th International Conference on Data Warehousing and Knowledge Discovery; 2014. p. 45–56.Google Scholar
  11. 11.
    Jensen MR, Møller TM, Pedersen TB. Specifying OLAP cubes on XML data. J Intell Inf Syst. 2001;17(2–3):255–80.CrossRefzbMATHGoogle Scholar
  12. 12.
    Kämpgen B, Stadtmüller S, Harth A. Querying the global cube: integration of multidimensional datasets from the web. In: Proceedings of 19th International Conference on Knowledge Engineering and Knowledge Management; 2014. p. 250–65.Google Scholar
  13. 13.
    Kimball R, Ross M. The data warehouse toolkit: the complete guide to dimensional modeling. 2nd ed. Wiley; 2002.Google Scholar
  14. 14.
    Golfarelli M, Mandreoli F, Penzo W, Rizzi S, Turricchia E. OLAP query reformulation in peer-to-peer data warehousing. Inf Syst. 2012;37(5):393–411.CrossRefGoogle Scholar
  15. 15.
    Lenzerini M. Data integration: a theoretical perspective. In: Proceedings of 21st ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2002. p. 233–46.Google Scholar
  16. 16.
    Malvestuto FM. The classification problem with semantically heterogeneous data. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1988. p. 157–76.Google Scholar
  17. 17.
    Mangisengi O, Huber J, Hawel C, Eßmayr W. A framework for supporting interoperability of data warehouse islands using XML. In: Proceedings of 3rd International Conference on Data Warehousing and Knowledge Discovery; 2001. p. 328–38.Google Scholar
  18. 18.
    Nebot V, Berlanga Llavori RB, Pérez-Martínez JM, Aramburu MJ, Pedersen TB. Multidimensional integrated ontologies: a framework for designing semantic data warehouses. J Data Semant. 2009;13:1–36.CrossRefGoogle Scholar
  19. 19.
    Olaru MO. Partial multi-dimensional schema merging in heterogeneous data warehouses. In: Proceedings of 31st International Conference on Conceptual Modeling; 2012. p. 563–71.Google Scholar
  20. 20.
    Pedersen TB, Shoshani A, Gu J, Jensen C8.S. Extending OLAP querying to external object databases. In: Proceedings of International Conference on Information and Knowledge Management; 2000. p. 405–13.Google Scholar
  21. 21.
    Rahm E, Bernstein PA. A survey of approaches to automatic schema matching. VLDB J. 2001;10(4):334–50.CrossRefzbMATHGoogle Scholar
  22. 22.
    Rizzi S, Abelló A, Lechtenbörger J, Trujillo J. Research in data warehouse modeling and design: dead or alive? In: Proceedings of ACM 9th International Workshop on Data Warehousing and OLAP; 2006. p. 3–10.Google Scholar
  23. 23.
    Sato H. Handling summary information in a database: derivability. In: Proceedings of ACM SIGMOD International Conference on Management of Data; 1981. p. 98–107.Google Scholar
  24. 24.
    Torlone R. Two approaches to the integration of heterogeneous data warehouses. Distrib Parallel Databases. 2008;23(1):69–97.CrossRefGoogle Scholar
  25. 25.
    Tseng FSC, Chen CW. Integrating heterogeneous data warehouses using XML technologies. J Inf Sci. 2005;31(3):209–29.CrossRefGoogle Scholar
  26. 26.
    Vetterli T, Vaduva A, Staudt M. Metadata standards for data warehousing: open information model vs. common warehouse metamodel. ACM SIGMOD Rec. 2000;29(3):68–75.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.University of RomeRomeItaly

Section editors and affiliations

  • Torben Bach Pedersen
    • 1
  • Stefano Rizzi
    • 2
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.DISIUniv. of BolognaBolognaItaly