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Conference Papers Year : 2014

Reducing Multidimensional Data


Our aim is to elaborate a multidimensional database reduction process which will specify aggregated schema applicable over a period of time as well as retains useful data for decision support. Firstly, we describe a multi-dimensional database schema composed of a set of states. Each state is defined as a star schema composed of one fact and its related dimensions. Each reduced state is defined through reduction operators. Secondly, we describe our experi-ments and discuss their results. Evaluating our solution implies executing different requests in various contexts: unreduced single fact table, unreduced re-lational star schema, reduced star schema or reduced snowflake schema. We show that queries are more efficiently calculated within a reduced star schema.
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Dates and versions

hal-01141433 , version 1 (13-04-2015)


  • HAL Id : hal-01141433 , version 1
  • OATAO : 13214


Faten Atigui, Franck Ravat, Jiefu Song, Gilles Zurfluh. Reducing Multidimensional Data. International Conference on Data Warehousing and Knowledge Discovery - DaWaK 2014, Sep 2014, Munich, Germany. pp. 208-220. ⟨hal-01141433⟩
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