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Using Semantics for Query Derivability in Data Warehouse Applications

  • J. Albrecht
  • W. Hämmer
  • W. Lehner
  • L. Schlesinger
Part of the Advances in Soft Computing book series (AINSC, volume 7)

Abstract

Materialized summary tables and cached query results are frequently used for the optimization of aggregate queries in a data warehouse. Query rewriting techniques are incorporated into database systems to use those materialized views and thus avoid accessing the possibly huge raw data. A rewriting is only possible if the query is derivable from these views. Several approaches can be found in the literature to check the derivability and find query rewritings. However, most algorithms either find rewritings only in very restricted cases or in complex cases which rarely occur in data warehouse environments. The specific application scenario of a data warehouse with its multidimensional perspective allows the consideration of much more semantic information, e.g. structural dependencies within the dimension hierarchies and different characteristics of measures. The motivation of this article is to use this information to present simple conditions for derivability in a large number of relevant cases which go beyond previous approaches.

Keywords

Data Warehouse Category Attribute Data Cube Fact Table Query Plan 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • J. Albrecht
  • W. Hämmer
  • W. Lehner
  • L. Schlesinger

There are no affiliations available

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