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

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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.