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