Hurtado, C.A. and Mendelzon, A.O. (2002) OLAP Dimension Constraints. Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, Madison, June 2002, 169-179.
https://doi.org/10.1145/543613.543636

has been cited by the following article:

• TITLE: Hierarchical Datacubes

  AUTHORS: Mickaël Martin Nevot, Sébastien Nedjar, Lotfi Lakhal

  KEYWORDS: ROLAP Cubing, Data Warehouse, Datacube, Big Data, Business Intelligence, Hierarchical Cube, Hierarchical Dimensions

  JOURNAL NAME: Journal of Computer and Communications, Vol.11 No.6, June 30, 2023

  ABSTRACT: Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these responses. To meet this need, we propose, in this paper, a complete redefinition of the framework and the formal definition of traditional database analysis through the prism of hierarchical dimensions. After characterizing the hierarchical data cube lattice, we introduce the hierarchical data cube and its most concise reduced representation, the closed hierarchical data cube. It offers compact replication so as to optimize storage space by removing redundancies of strongly correlated data. Such data are typical of data warehouses, and in particular in video games, our field of study and experimentation, where hierarchical dimension attributes are widely represented.