Modeling Transformations between Versions of a Temporal Data Warehouse

  • Johann Eder
  • Karl Wiggisser
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5232)


Data warehouses are oftentimes in charge of supporting the decision finding processes in companies and public administration. To fulfil these tasks the systems model parts of real world regarding the respective application domain. But the world is changing and in such an evolving environment data warehouses can only cope with their tasks if they can be kept consistent with the real world. For that purpose they have to be able to deal with modifications in the data schema and their influence on the data values. This paper focusses on the modeling of the data transformation. We identified six different types of transformation operations. Based on the semantic analysis of these operations we present a matrix based representation for data transformation which enables us to specify the transformation results and a more efficient graph based representation, which furthermore offers potential for optimization.


Data Warehouse Maintenance Temporal Data Warehouses Data Transformation 


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  1. 1.
    Statistical Office of the European Communities,
  2. 2.
    Eder, J., Wiggisser, K.: Data Warehouse Maintenance, Evolution and Versioning. In: Liu, L., Özsu, T. (eds.) Encyclopedia of Database Systems. LNCS. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    Eder, J., Koncilia, C., Morzy, T.: The COMET Metamodel for Temporal Data Warehouses. In: Pidduck, A.B., Mylopoulos, J., Woo, C.C., Ozsu, M.T. (eds.) CAiSE 2002. LNCS, vol. 2348, pp. 83–99. Springer, Heidelberg (2002)Google Scholar
  4. 4.
    Eder, J., Wiggisser, K.: A DAG Comparison Algorithm and its Application to Temporal Data Warehousing. In: ER 2006, pp. 217–226. Springer, Heidelberg (2006)Google Scholar
  5. 5.
    Kimball, R.: Slowly Changing Dimensions. DBMS Magazine 9(4), 14 (1996)Google Scholar
  6. 6.
    Quix, C.: Repository Support for Data Warehouse Evolution. In: Proc. of the 1st Int’l. WS on Design and Management of Data Warehouses, p. 4 (1999)Google Scholar
  7. 7.
    Vaisman, A., Mendelzon, A.: A Temporal Query Language for OLAP: Implementation and a Case Study. In: Proc. of the Int’l. WS on Database Programming Languages, pp. 78–96 (2001)Google Scholar
  8. 8.
    Body, M., Miquel, M., Bédard, Y., Tchounikine, A.: Handling Evolutions in Multidimensional Structures. In: Proc. of the 19th ICDE, pp. 581–591 (2003)Google Scholar
  9. 9.
    Golfarelli, M., Lechtenbörger, J., Rizzi, S., Vossen, G.: Schema versioning in data warehouses: Enabling cross-version querying via schema augmentation. Data- and Knowledge Engineering 59(2), 435–459 (2006)CrossRefGoogle Scholar
  10. 10.
    Malinowski, E., Zimányi, E.: A conceptual solution for representing time in data warehouse dimensions. In: Proc. of the 3rd Asia-Pacific Conference on Conceptual Modelling, pp. 45–54 (2006)Google Scholar
  11. 11.
    Kaas, C., Pedersen, T., Rasmussen, B.: Schema Evolution for Stars and Snowflakes. In: Proc. of the 6th ICEIS, pp. 425–433 (2004)Google Scholar
  12. 12.
    SAP Inc.: Multi-Dimensional Modeling with BW: ASAP for BW Accelerator. Technical report, SAP Inc. (2000)Google Scholar
  13. 13.
    Kalido: KALIDO Dynamic Information Warehouse: A Technical Overview. Technical report, Kalido (2004)Google Scholar
  14. 14.
    Ehrig, H., Ehrig, K., Prange, U., Taentzer, G.: Fundamentals of Algebraic Graph Transformation. Monographs in Theoret. Comp. Science (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Johann Eder
    • 1
  • Karl Wiggisser
    • 1
  1. 1.Inst. of Informatics-SystemsAlps-Adria University KlagenfurtAustria

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