Fuzzy Data Warehousing for Performance Measurement: Concept and Implementation

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.

 

1118037315
Fuzzy Data Warehousing for Performance Measurement: Concept and Implementation

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.

 

99.0 In Stock
Fuzzy Data Warehousing for Performance Measurement: Concept and Implementation

Fuzzy Data Warehousing for Performance Measurement: Concept and Implementation

by Daniel Fasel
Fuzzy Data Warehousing for Performance Measurement: Concept and Implementation

Fuzzy Data Warehousing for Performance Measurement: Concept and Implementation

by Daniel Fasel

eBook2014 (2014)

$99.00 

Available on Compatible NOOK devices, the free NOOK App and in My Digital Library.
WANT A NOOK?  Explore Now

Related collections and offers


Overview

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.

 


Product Details

ISBN-13: 9783319042268
Publisher: Springer-Verlag New York, LLC
Publication date: 07/08/2014
Series: Fuzzy Management Methods
Sold by: Barnes & Noble
Format: eBook
Pages: 236
File size: 3 MB

About the Author

Dr. Daniel Fasel is the founder, CEO and President of the Managerial Board at Scigility. Previously, he served as the first data scientist on the business intelligence team at Swisscom and was key in implementing NoSQL technologies for explorative analytics during his time there. Before focusing on data science and NoSQL technologies, he was a BI Engineer for the contract and customer field - a core component of the Swisscom Data Warehouse. He also served as a BI Architect and Administrator for the Oracle Hyperion Essbase cubes. In 2012, he received his Ph.D. in economics from the University of Fribourg.

 

Table of Contents

​Introduction.- Fundamental Concepts.- Fuzzy Data Warehouse.- Application of Fuzzy Data Warehouse.- Implementation.- Evaluation and Conclusion.
From the B&N Reads Blog

Customer Reviews