원문정보
초록
영어
In recent years, decision support systems otherwise called business Intelligence[BI] have become an integral part of organization's decision making strategy. Organizations nowadays are competing in the global market. In order for a company to gain competitive advantage over the others and also to help make better decisions, Data warehousing cum Data Mining are now playing a significant role in strategic decision making. It helps companies make better decisions, streamline work-flows, provide better customer services, and target market their products and services. The use of data warehousing and BI technology span sectors such as retail, airline, banking, health, government, investment, insurance, manufacturing, telecommunication, transportation, hospitality, pharmaceutical, and entertainment. This paper gives the report about developing data warehouse for business management using the Federal University of Technology Student-Course management system as a case study. It describes the process of data warehouse design and development using Microsoft SQL Server Analysis Services. It also outlines the development of a data cube as well as application of Online Analytical processing (OLAP) tools and Data Mining tools in data analysis. The purpose of the paper is to present the benefits of data warehouse and to sensitize companies in Nigeria to start building these facilities into their Enterprise resource management systems with the aim of making effective business decisions that will promote the rapid growth of the companies.
목차
1. Introduction
The future of data warehousing
Data Mining
How data mining works
Data mining consists of five major elements:
Different levels of analysis are available:
2. The Case Project of Student Course Management System
The Motivation for the Data Warehouse system.
3. Designing the Data Warehouse
The logical Design
Dimensional Data Modeling approach
The Star Schema:
4. Data Warehouse Implementation
Transporting Data from OLTP Database to Data Warehouse
FactTable
5. Online Analytical Process (OLAP)
5.1 The Data Analysis Reports
6. The Data mining (Discovering Hidden knowledge using Data Mining)
7. Conclusion and Discussion
References
