Enhancing flexibility and expressivity of contextual hierarchies

Data warehouses are nowadays extensively used to perform analyses on huge volume of data. This success is partly due to the capacity of considering data at several granularity levels thanks to the use of hierarchies. However, in previous work, we showed that the experts knowledge was not much considered in the generalization process. To overcome this drawback, we introduced a new category of hierarchies, namely the contextual hierarchies. Unfortunately, in contrast to the complexity of expert knowledge that should be considered, the knowledge definition process was too rigid. In this paper, we extend these hierarchies and their related techniques to drastically increase their flexibility and expressivity. To this purpose, we adopt a fuzzy-based methodology which allows to express expert knowledge in a very convenient way. Experiment results obtained on synthetic datasets show that the contextual generalization process is very fast and can thus be used in practice.