Increase the visibility of your scientific production by authorizing the export of your publications to HAL!

Reducing Multidimensional Data

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

[img]
Preview
Text
Download (1MB) | Preview

Abstract

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)
Language: English
Date: 2014
Uncontrolled Keywords: Multidimensional design - Data reduction - Experimental assessment
Subjects: H- INFORMATIQUE
Divisions: Institut de Recherche en Informatique de Toulouse
Site: UT1
Date Deposited: 27 Mar 2019 14:15
Last Modified: 27 Mar 2019 14:15
URI: http://publications.ut-capitole.fr/id/eprint/29827

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year