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Interoperability in Data Warehouses

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Synonyms

Data warehouse integration

Definition

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....

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  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  3. Banek M, Vrdoljak B, Min Tjoa A, Skocir Z. Automating the schema matching process for heterogeneous data warehouses. In: Proceedings of the 9th International Conference on Data Warehousing and Knowledge Discovery; 2007. p. 45–54.

    Google Scholar 

  4. Bergamaschi S, Olaru M. O., Sorrentino S, Vincini M. Dimension matching in Peer-to-Peer Data Warehousing. In: Proceedings of the 8th International Conference on Decision Support Systems; 2012. p. 149–60.

    Google Scholar 

  5. Berger S, Schrefl M. Analysing multi-dimensional data across autonomous data warehouses. In: Proceedings of the 8th International Conference on Data Warehousing and Knowledge Discovery; 2006. p. 120–33.

    Chapter  Google Scholar 

  6. Berger S, Schrefl M. FedDW global schema architect: UML-based design tool for the integration of data mart schemas. In: Proceedings of the 15th International Workshop on Data warehousing and OLAP; 2012. p. 33–40.

    Google Scholar 

  7. Cabibbo L, Torlone R. On the integration of autonomous data marts. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management; 2004. p. 223–34.

    Google Scholar 

  8. Cabibbo L, Torlone R. Integrating heterogeneous multidimensional databases. In: Proceedings of the 17th International Conference on Scientific and Statistical Database Management; 2005. p. 205–14.

    Google Scholar 

  9. Cabibbo L, Panella I. Torlone R. DaWaII: a tool for the integration of autonomous data marts. In: Proceedings of the 22nd International Conference on Data Engineering, Demo session; 2006.

    Google Scholar 

  10. Etcheverry L, Vaisman A, Zimányi E. Modeling and querying data warehouses on the semantic web using QB4OLAP. In: Proceedings of the 16th International Conference on Data Warehousing and Knowledge Discovery; 2014. p. 45–56.

    Google Scholar 

  11. Jensen MR, Møller TM, Pedersen TB. Specifying OLAP cubes on XML data. J Intell Inf Syst. 2001;17(2–3):255–80.

    Article  MATH  Google Scholar 

  12. Kämpgen B, Stadtmüller S, Harth A. Querying the global cube: integration of multidimensional datasets from the web. In: Proceedings of the 19th International Conference on Knowledge Engineering and Knowledge Management; 2014. p. 250–65.

    Google Scholar 

  13. Kimball R, Ross M. The data warehouse toolkit: the complete guide to dimensional modeling. 2nd ed. Wiley; 2002.

    Google Scholar 

  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.

    Article  Google Scholar 

  15. Lenzerini M. Data integration: a theoretical perspective. In: Proceedings of the 21st ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems; 2002. p. 233–46.

    Google Scholar 

  16. Malvestuto FM. The classification problem with semantically heterogeneous data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1988. p. 157–76.

    Google Scholar 

  17. Mangisengi O, Huber J, Hawel C, Eßmayr W. A framework for supporting interoperability of data warehouse islands using XML. In: Proceedings of the 3rd International Conference on Data Warehousing and Knowledge Discovery; 2001. p. 328–38.

    Chapter  MATH  Google Scholar 

  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.

    Google Scholar 

  19. Olaru MO. Partial multi-dimensional schema merging in heterogeneous data warehouses. In: Proceedings of the 31st International Conference on Conceptual Modeling; 2012. p. 563–71.

    Chapter  Google Scholar 

  20. Pedersen TB, Shoshani A, Gu J, Jensen C8.S. Extending OLAP querying to external object databases. In: Proceedings of the 9th International Conference on Information and Knowledge Management; 2000. p. 405–13.

    Google Scholar 

  21. Rahm E, Bernstein PA. A survey of approaches to automatic schema matching. VLDB J. 2001;10(4):334–50.

    Article  MATH  Google Scholar 

  22. Rizzi S, Abelló A, Lechtenbörger J, Trujillo J. Research in data warehouse modeling and design: dead or alive? In: Proceedings of the ACM the 9th International Workshop on Data Warehousing and OLAP; 2006. p. 3–10.

    Google Scholar 

  23. Sato H. Handling summary information in a database: derivability. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1981. p. 98–107.

    Google Scholar 

  24. Torlone R. Two approaches to the integration of heterogeneous data warehouses. Distrib Parallel Databases. 2008;23(1):69–97.

    Article  Google Scholar 

  25. Tseng FSC, Chen CW. Integrating heterogeneous data warehouses using XML technologies. J Inf Sci. 2005;31(3):209–29.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

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Correspondence to Riccardo Torlone .

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Torlone, R. (2018). Interoperability in Data Warehouses. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_207

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