@article{869,
  author = {Sami Naouali},
  title = {Toward Reducing Data Cubes},
  journal = {Journal of Data Processing},
  year = {2012},
  volume = {2},
  number = {2},
  doi = {},
  url = {http://www.dline.info/jdp/fulltext/v1n2/3.pdf},
  abstract = {This paper presents an approach toward reducing multidimensional data in a data warehouse and giving theanalyst more facilities when querying data cubes. This reduction concerns the number of dimensions as well as the hugeamount of facts in the data warehouse. To make it possible, this approach combines the potential of the Principal ComponentAnalysis which is a purely statistical approach with the Rough Set Theory which is an Artificial Intelligence one. In fact, ThePrincipal Component Analysis is used, here, to reduce dimensions in the data warehouse without loosing information. So itcomputes a subset of the most important and pertinent dimensions. This subset of data dimensions is used by the Rough SetTheory in order to delete superfluous and redundant facts. The interest of this approach is to help user to focus on the mostimportant information of the data warehouse which is a very hard task when working on the whole multidimensionaldatabase.},
}