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Reducing Multidimensional Data

Atigui, Faten and Ravat, Franck and Song, Jiefu and Zurfluh, Gilles Reducing Multidimensional Data. (2014) In: International Conference on Data Warehousing and Knowledge Discovery - DaWaK 2014, 2 September 2014 - 4 September 2014 (Munich, Germany).

(Document in English)

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Official URL: http://dx.doi.org/10.1007/978-3-319-10160-6_19


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.

Item Type:Conference or Workshop Item (Paper)
Additional Information:Thanks to Springer editor. The definitive version is available at http://www.springer.com/fr/book/9783319101590 The original PDF of the article can be found at http://link.springer.com/chapter/10.1007/978-3-319-10160-6_19
HAL Id:hal-01141433
Audience (conference):International conference proceedings
Uncontrolled Keywords:
Institution:Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Laboratory name:
Deposited On:13 Apr 2015 06:25

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