Fuzzy data warehousing for performance measurement (Record no. 14661)

MARC details
000 -LEADER
fixed length control field 01978nam a2200277Ia 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20250711162917.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 220909b |||||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319042251
037 ## - SOURCE OF ACQUISITION
Terms of availability Textual
040 ## - CATALOGING SOURCE
Original cataloging agency CSL
Language of cataloging eng
Transcribing agency CSL
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title eng
084 ## - COLON CLASSIFICATION NUMBER
Classification number B217:(D65,8(B)) Q4
Assigning agency CSL
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Fasel, Daniel
Relator term Author.
9 (RLIN) 815415
245 #0 - TITLE STATEMENT
Title Fuzzy data warehousing for performance measurement
Remainder of title : Concept and implementation
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. New York :
Name of publisher, distributor, etc. Springer,
Date of publication, distribution, etc. 2014.
300 ## - PHYSICAL DESCRIPTION
Extent xxiv, 236p.
Other physical details ; ill.
500 ## - GENERAL NOTE
General note Appendices A-D195-230p.; Bibliography 231-236p.
520 ## - SUMMARY, ETC.
Summary, etc. 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.
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Architectural overview
9 (RLIN) 815416
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Fuzzy data warehouse
9 (RLIN) 815417
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Movie rental company
9 (RLIN) 815418
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data warehouse concepts
9 (RLIN) 815419
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Classification part B217:(D65,8(B)) Q4
Koha item type Textual
Source of classification or shelving scheme Colon Classification (CC)
Suppress in OPAC No
Holdings
Withdrawn status Lost status Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Full call number Barcode Date last seen Price effective from Koha item type
        Central Science Library Central Science Library 2022-09-12 314, 10/03/2015, Vardhman Books   B217:(D65,8(B)) Q4 SL1598011 2022-09-12 2022-09-12 Textual
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