Enterprise BI and data analysis platform using SAP HANA (SAP enterprise BI using NetWeaver) (Record no. 6056)

MARC details
000 -LEADER
fixed length control field 02192nam a22001937a 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220107122838.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 180524b xxu||||| |||| 00| 0 eng d
040 ## - CATALOGING SOURCE
Transcribing agency IIITMK
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Merly S Joseph (92216016)
9 (RLIN) 14036
245 ## - TITLE STATEMENT
Title Enterprise BI and data analysis platform using SAP HANA (SAP enterprise BI using NetWeaver)
300 ## - PHYSICAL DESCRIPTION
Extent MSC DA 2016-2018
500 ## - GENERAL NOTE
General note Business Intelligence and Big Data have become increasingly important over the past two decades.<br/>Although there is a great advance in technology, but the rate at which business data is growing is<br/>much higher. Business as a whole revolves around 3 words –customer, sales and purchase. Trying<br/>SAP’s latest BI integrated technology platform –NetWeaver- was an all-new experience.<br/>NetWeaver was initially connected to SAP HANA, which is the master database for a SAP ERP<br/>system. After implementing SAP HANA, NetWeaver was configured. The raw data was then<br/>loaded into NetWeaver for removing redundancy in data. This is possible through NetWeaver’s<br/>major component InfoCubes, which enables a 3D object alignment of data. The data retrieved<br/>through InfoCubes is the ultimate redundant data that can be used for predictive analysis.<br/>Predictive analytics extracts information from data sets in order to discover complex relationships,<br/>recognize unknown patterns, forecasting actual trends, find associations, etc. This allows us to<br/>anticipate the future and make the right decisions. Here the historical sales data for 45 stores<br/>located in different regions has been provided. Each store contains a number of departments.<br/>Predicting the department-wide sales for each store for the following year, modelling the effects<br/>of markdowns on holiday weeks, providing recommended actions based on the insights drawn,<br/>with prioritization placed on largest business impact were what we wanted to achieve through this<br/>project. Undoubtedly, there is more scope to this project since business data is dynamic and keeps<br/>on evolving.
502 ## - DISSERTATION NOTE
Degree type MSC DA
Name of granting institution 2016-2018
Year degree granted INT
-- Mr. Vishnu Kabil
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element BUSINESS INTELLIGENCE
9 (RLIN) 14037
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element SAP HANA
9 (RLIN) 14038
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element NETWEAVER
9 (RLIN) 14039
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Home library Current library Shelving location Date acquired Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Non Fiction IIITM-K Kerala University of Digital Sciences, Innovation and Technology Knowledge Centre   24/05/2018   R-1363 24/05/2018 24/05/2018 Project Reports