A Data Warehouse Model for Integrating Fuzzy Concepts in Meta Table Structures

In classical data warehouses (DWH), classification of values takes place in a sharp manner, because of this true values cannot be measured and smooth transition between classes does not occur. In this paper, a fuzzy data warehouse (FDWH) modeling approach, which allows integration of fuzzy concepts without affecting the core of a DWH is presented. This is accomplished through the addition of a meta-table structure, which enables integration of fuzzy concepts on dimensions and facts, while preserving the time-invariability of the DWH and allowing analysis of data both sharp and fuzzy. A comparison to existing approaches for integrating fuzzy concepts in DWH is presented. Guidelines for modeling the fuzzy meta-tables and a meta-model for the FDWH are also outlined in this paper. The use of the proposed approach is demonstrated by a retail company example. Finally, a comparison of fuzzy and classical data warehousing approaches is presented.