Intelligent Techniques for Data- Information- Knowledge Evolution
| Main Author: | |
|---|---|
| Language: | English |
| Published: |
The Ohio State University / OhioLINK
2011
|
| Subjects: | |
| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=osu1306860521 |
| id |
ndltd-OhioLink-oai-etd.ohiolink.edu-osu1306860521 |
|---|---|
| record_format |
oai_dc |
| spelling |
ndltd-OhioLink-oai-etd.ohiolink.edu-osu13068605212021-08-03T06:02:58Z Intelligent Techniques for Data- Information- Knowledge Evolution Agrawal, Artika Computer Science The quality of data in an Enterprise Data Warehouse (EDW) plays a very important rolein decision making activities like cross selling products and services or to identify unmetmarket needs etc. Every organization puts in a lot of resources to maintain data of highquality, in order to make informed decisions regarding market trends or to create businessstrategies. A lot of work is done to identify the types of data quality issues occurring indata warehouses and a large number of industry best practices and standards have beenlaid down to improve data quality. However, not much work is done for continuous dataquality management and improvement particularly in customer data domain.This work begins with the identification of 1) various types of data quality errors in acustomer-centric financial database that are introduced in an on-going bases 2) identifiesthe root causes of those error types and 3) the corrective actions. We have also proposeda framework for continuous improvement and governance of EDW that achieves greaterlevels of traceability and decision making across four different organizational levels-Infrastructure, Operations, Business and Strategy. The database used for this purpose is areal-world EDW which is being fed by multiple legacy source systems that continuouslyintroduce redundant customer instances causing data duplication. To address this problemof duplicated data we have used neural networks for customer classification which ensure 2011-07-27 English text The Ohio State University / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=osu1306860521 http://rave.ohiolink.edu/etdc/view?acc_num=osu1306860521 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Computer Science |
| spellingShingle |
Computer Science Agrawal, Artika Intelligent Techniques for Data- Information- Knowledge Evolution |
| author |
Agrawal, Artika |
| author_facet |
Agrawal, Artika |
| author_sort |
Agrawal, Artika |
| title |
Intelligent Techniques for Data- Information- Knowledge Evolution |
| title_short |
Intelligent Techniques for Data- Information- Knowledge Evolution |
| title_full |
Intelligent Techniques for Data- Information- Knowledge Evolution |
| title_fullStr |
Intelligent Techniques for Data- Information- Knowledge Evolution |
| title_full_unstemmed |
Intelligent Techniques for Data- Information- Knowledge Evolution |
| title_sort |
intelligent techniques for data- information- knowledge evolution |
| publisher |
The Ohio State University / OhioLINK |
| publishDate |
2011 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=osu1306860521 |
| work_keys_str_mv |
AT agrawalartika intelligenttechniquesfordatainformationknowledgeevolution |
| _version_ |
1719430027226906624 |