Abstract:
A data warehouse is used intensively in many industry domains to gain competitive advantage over its
competitors. In modern data warehouses, linguistic analytics is an important aspect, so that it has the ability to take
more precious decisions. In most of the data warehouse implementations, it is designed for crisp analysis. Crisp
analysis has its own limitations and boundaries with the major assumptions that every situation belongs to one state
and denial to other states. Hence, crisp data warehouse does not allow to carry out linguistic analytics. When a
fuzzy data warehouse is implemented, because of the fuzzy nature of the data warehouse, linguistic analytics can be
done to a certain extent. In this research, non-functional requirements such as performance and configuration are
also covered so that this method can be implemented in the real world
