Volume 26, Issue 2 (2015), pp. 283–312
This paper presents a novel approach to the adaptation of multidimensional data models to user-specific needs. The multidimensional data models used in contemporary business-intelligence systems are inherently complex. In order to reduce the complexity of these models, we propose using a qualitative multiple-criteria decision modelling method that is based on using a hierarchical tree of the criteria to decompose the larger problem into a group of smaller problems. The final value is derived by aggregating the criteria values using simple “if-then” rules, which form the knowledge-based expert rules in the hierarchical criteria tree that reflect users’ preferences. The multiple-criteria analysis of the multidimensional model structure results in a multidimensional model that exhibits a reduced complexity and is adapted to users’ needs. The model was validated using sales data from a medium-size enterprise. The qualitative (through questionnaires) and the quantitative (through usage mining) evaluation of the proposed methodology both showed that the proposed approach increases the ease-of-use of business intelligence systems and also contributes to a higher user satisfaction.
Pub. online:1 Jan 2009Type:Research ArticleOpen Access
Volume 20, Issue 1 (2009), pp. 35–50
We tested the ability of humans and machines (data mining techniques) to assign stress to Slovene words. This is a challenging comparison for machines since humans accomplish the task outstandingly even on unknown words without any context. The goal of finding good machine-made models for stress assignment was set by applying new methods and by making use of a known theory about rules for stress assignment in Slovene. The upgraded data mining methods outperformed expert-defined rules on practically all subtasks, thus showing that data mining can more than compete with humans when constructing formal knowledge about stress assignment is concerned. Unfortunately, compared to humans directly, the data mining methods still failed to achieve as good results as humans on assigning stress to unknown words.