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博碩士論文 etd-0803115-143153 詳細資訊
Title page for etd-0803115-143153
論文名稱
Title
商業智慧之資料塑模方法論─以A公司之客訴系統為例
A Methodology of Data Modeling for Business Intelligence: A Case Study of A Company’s Customer Complaint System
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
120
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2015-06-26
繳交日期
Date of Submission
2015-09-03
關鍵字
Keywords
資料蒐集、資訊分析、資料倉儲的生命週期模型、設計科學研究法、產出資料模型
Building the Data Model, Data Warehouse Lifecycle, Design Science Research Methodology, Data Analysis, Data Collection
統計
Statistics
本論文已被瀏覽 5842 次,被下載 1589
The thesis/dissertation has been browsed 5842 times, has been downloaded 1589 times.
中文摘要
企業內部許多現存的資料或系統相關文件已經具有充足的價值,這些價值與決策者執行決策息息相關,因此找尋有意義的商業智慧是目前企業生存的主要重點之一,本研究將利用設計科學研究法 (Design Science Research Methodology, DSRM)歸納出一套商業智慧的資料塑模方法論,其中說明了資料蒐集、產出資料模型及資訊分析的方法與步驟,並運用此方法論協助個案A公司解決客訴處理流程所面臨的問題,以驗證本方法論之可行性。此方法論最終的研究成果可協助未來企業進行內部的商業智慧系統規劃與分析,讓企業內部人員透過維度模型及多維度資料立方體來發掘客訴流程中潛在的智慧,助於公司瞭解方法論各階段在業界的實際運作情形,並且可依循本方法論之步驟進行資訊萃取與塑模,提升企業決策者進行決策的效率,同時使各部門人員一致性解讀資訊,提升客訴流程的進行與管控,同時提高客戶的忠誠度。
Abstract
There are so many data and document remain in the business, and they have sufficient values. These values are closely when the decision maker make decisions. So looking for the meaningful business intelligence is the main point to the business. This research will use the design science research methodology to generalize a methodology of data modeling for business intelligence, it includes the methods and steps of data collection, building the data model and data analysis, it also help A company solve the problem of customer complaint process to verify the feasibility of the methodology.
The final research result of this methodology can help business to conduct in planning and analyzing the business intelligence system in the future, it also can let the staff to use the dimension model and multi-dimension data cube to explore the hidden intelligence in the customer complaint process. That will help the business to understand the real operating condition in steps of methodology, it also can help them follow the steps of this methodology to conduct in data collection and data modeling. That will improve the effectiveness when the decision makers make decisions, it also make the staff of any apartments to read the information consistently at the same time. At last, it will also improve the progress and coercion of the customer complaint process, and increase the customer loyalty simultaneously.
目次 Table of Contents
論文審定書+i
誌謝+ii
摘要+iii
目錄+v
圖次+vii
表次+ix
第一章 緒論+1
第一節 研究背景與動機+1
第二節 研究目的與範圍+4
第二章 文獻探討+5
第一節 商業智慧+5
第二節 資料擷取、轉換及載入+9
第三節 資料倉儲+12
第四節 資料倉儲專案的生命週期模型+16
第五節 線上分析處理+29
第三章 研究方法+32
第一節 設計科學研究法+32
第二節 研究方法與步驟+34
第四章 方法論發展+38
第一節 方法論發展過程+38
第二節 資料蒐集階段+42
第三節 產出資料模型階段+44
第四節 資訊分析階段+53
第五章 案例展示+60
第一節 個案背景+60
第二節 資料蒐集階段+61
第三節 產出資料模型階段+69
第四節 資訊分析階段+97
第五節 方法論產出內容評估+103
第六章 結論+104
參考文獻+107
中文參考文獻+107
英文參考文獻+107
參考文獻 References
中文參考文獻

吳仁和,《資訊管理-企業創新與價值創造》,5th Edition,台北市:智勝文化,2014年6月。

張慶樟,「低成本、高彈性營運策略下的商業智慧系統」,國立中山大學高階經營碩士學程在職專班碩士論文,2012年6月。

黃惠婷,「商業智慧系統建置以H公司為個案研究」,國立交通大學資訊管理學程碩士論文,2009年6月。

吳宜霖,「運用MDA於主管資訊系統開發之研究─以遊艇廠為例」,國立中山大學資訊管理學系碩士論文,2014年6月。

夏則智,「線上分析處理之彈性報表設計方法論」,國立中山大學資訊管理研究所博士論文,2004年1月。

張慶樟,「低成本、高彈性營運策略下的商業智慧系統」,國立中山大學高階經營碩士學程在職專班碩士論文,2012年6月。

英文參考文獻

Negash, S., “Business Intelligence,” Communications of the Association for Information Systems, Vol. 13, No. 15, Febuary 2004, pp. 177-195.

Luhn, H. P., “A Business Intelligence System,” IBM Journal of Research and Development, Vol. 2, No. 4, October 1958, pp. 314-319.

Murugan, and Asokan, Business Intelligence Techniques: A Perspective from Accounting and Finance, New York, Springer, 2003.

Liya Wu, Barash, G., and Bartolini, C., “A Service-oriented Architecture for Business Intelligence,” Service-Oriented Computing and Applications, June 2007, pp. 279-285.

Rasmussen, N., Paul, S., and Per O.Solli, Financial Business Intelligence, New York, John Wiley & Sons, 2002.

Morris, H., “Bringing Business Objects into Extract-Transform-Load (ETL) Technology,” e-Business Engineering, October 2008, pp. 709-714.

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Thomsen, E., OLAP Solutions: Building Multidimensional Information Systems, Second Edition, Canada, John Wiley & Sons, 2002.

Torben, B., “A Foundation For Capturing and Querying Complex Multidimensional Data ,” Information Systems, Vol. 26 No. 5, July 2001, pp. 383-423.

Vassiliadis, P., “A Survey of Extract-Transform-Load Technology,” International Journal of Data Warehousing & Mining, Vol. 5 No. 3, September 2009, pp. 1-27.

Turban, E., Decision Support and Business Intelligence Systems, New Jersey, Pearson, 2006

Kimball, R., and Caserta, J., The Data Warehouse ETL Tookit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data, New York, John Wiley & Sons, 2004.

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Celina, M., Olszak, and Ewa, Z. “Approach to Building and Implementing Business Intelligence Systems,” Interdisciplinary Journal of Information, Knowledge, and Management, Vol. 2, 2007

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