MARC Record from marc_nuls

LEADER: 02166cam 2200337Ii 4500
001 9925181401001661
005 20150423154743.0
008 141028s2014 sz a b 000 0 eng d
019 $a866931314
020 $a9783319042251
020 $a3319042254
035 $a(OCoLC)893908026$z(OCoLC)866931314
035 $a(OCoLC)ocn893908026
040 $aUHC$erda$cUHC$dBTCTA$dUKMGB$dYDXCP$dOCLCF
050 4 $aQA76.9.D37 2014
082 04 $a511.3/223$223
100 1 $aFasel, Daniel,$eauthor.
245 10 $aFuzzy data warehousing for performance measurement :$bconcept and implementation /$cDaniel Fasel.
264 1 $aCham :$bSpringer,$c2014.
300 $axxiv, 236 pages :$billustrations ;$c25 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
490 1 $aFuzzy Management Methods,$x2196-4130
504 $aIncludes bibliographical references.
520 $aThe 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 0 $aData warehousing.
650 0 $aFuzzy systems.
830 0 $aFuzzy management methods,$x2196-4130
947 $cBOOK$fBOOK-SETC$g129.00$hCIRCSTACKS$iaa/sf$lNULS$o20141209$q1
980 $a99960955982