title: Hierarchical reorganization of dimensions in OLAP visualizations.
abstract: In this paper, we propose a new method for the visual reorganization of online analytical processing (OLAP) cubes that aims at improving their visualization. Our method addresses dimensions with hierarchically organized members. It uses a genetic algorithm that reorganizes k-ary trees. Genetic operators perform permutations of subtrees to optimize a visual homogeneity function. We propose several ways to reorganize an OLAP cube depending on which set of members is selected for the reorganization: all of the members, only the displayed members, or the members at a given level (level by level approach). The results that are evaluated by using optimization criteria show that our algorithm has a reliable performance even when it is limited to 1 minute runs. Our algorithm was integrated in an interactive 3D interface for OLAP. A user study was conducted to evaluate our approach with users. The results highlight the usefulness of reorganization in two OLAP tasks.
authors.affiliation: University François-Rabelais of Tours, Tours.
authors.forename: Sébastien
authors.initials: S
authors.lastname: Lafon
authors.forename: Fatma
authors.initials: F
authors.lastname: Bouali
authors.forename: Christiane
authors.initials: C
authors.lastname: Guinot
authors.forename: Gilles
authors.initials: G
authors.lastname: Venturini