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Presents an innovative approach for qualitative data analysis that is close to human reasoning
A practical use case example explains how to integrate fuzzy concepts in existing data warehouses
Provides a fuzzy data warehouse architecture overview using common open-source technologies
The numeric values retrieved from a data warehouse may be difficult for business users to interpret, and may even be interpreted incorrectly. Therefore, in order to better understand numeric values, business users may require an interpretation in meaningful, non-numeric terms. However, if the transition between non-numeric terms is crisp, true values cannot be measured and a smooth transition between classes may no longer be possible.This book addresses this problem by presenting a fuzzy classification-based approach for a data warehouses. Moreover, it introduces a modeling approach for fuzzy data warehouses that makes it possible to integrate fuzzy linguistic variables in a meta-table structure. The essence of this structure is that fuzzy concepts can be integrated into the dimensions and facts of an existing classical data warehouse without affecting its core. This allows a simultaneous analysis, both fuzzy and crisp. A case study of a movie rental company underlines and exemplifies the proposed approach.
Content Level »Research
Keywords »Data analytics - Data warehouse - Fuzzy classification - Fuzzy logic - Fuzzy set theory - OLAP application - Soft computing