Earticle

초록

영어

Large organizations have a lot of data. The data can be stored in many formats including data bases and unstructured file. This data bases must be collected, compared and made to work as a seamless whole but the different databases communicate well. A Data warehouse is an integrated collection of subject- oriented data in the support of decision making. The integration of data sources is achieved through the use of ETL (Extract, Transform and load) process. It is therefore extensively recognized that the appropriate design of ETL process are key factors in success of Data Warehouse Project. Data warehouse is used to provide effective result from multi- dimensional data analysis. Defective data lead to break downs in the supply chain, poor business decisions and inferior customer relationship management. So data quality is the degree to which data meet the specific needs of the customer. The accuracy and correctness of the results depend on the quality of the data. Improving the quality of data is important in data warehouse because it is used in the process decision support which requires accurate data. This project presents a data warehouse construction with quality decision support system to “Manage results for an organization using customer care center”. Organization used to maintain customer care to support and handle customer queries, to maintain details of customers, to provide frequent information regarding to their premiums, loans. This project determines a detail report such as how many customers are there in an Organization. How many customers paid full premiums, what are their dues, total amount paid? Which locations customer exists? How many customers are more valued customers? Total amount credited in organizations quarterly, what percent is gain/loss. In this paper we take source as flat files, relational tables and the data is extracted in staging area and then it is loaded in to a data warehouse. The different five themes frame our analysis is: Integration, Implementation, Intelligence, and Innovation and quality. The factors Definition conformance, completeness, validity, accuracy, non- duplication, accessibility applied on data warehouse dynamically to improve the performance of data warehouses.

목차

Abstract
 1. Introduction
 2. ETL Overview
 3. File Naming Convention
 4. Mapping Specification
  4.1. Sources
  4.2. Targets
 5. Auditing and Balancing
 6. Reasons for Holding the ETL Process
 7. Error Detection and Capture Design
  7.1. Transformation Errors
  7.2. Business Logic Errors
 8. Dimensional Modeling in Data Warehouses
  8.1.1. Fact Table
  8.2. Star Schema
  8.3. Snowflake Schema
 9. Data Warehouse Quality Factors
 10. Results
  10.1. Star Schema
  10.2.1. Objective
  10.3. Non Duplication
 11. Conclusion
 References

키워드

저자

• Kushanoor Akbar [ JNTUA, Anantapur university, Madanapalle Institute of Technology & Science ]
• Dr. S.Murali Krishna [ JNTUA, Anantapur university, Professor and Head in CSE Dept, Madanapalle Institute of Technology & Science ]
• T. Vidya Sagar Reddy [ Senior Data Warehouse consultant, Capgemini PVT LTD, Bangalore, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605