近年來，資料倉儲技術漸趨成熟，許多行業均陸續完成系統導入應用，藉此改善或增進企業經營效率。在半導體製造產業中，也陸續興起導入資料倉儲系統的熱潮。然而，資料倉儲往往是巨額的資訊技術投資，要在維持晶圓廠持續生產運轉的條件下完成資料倉儲系統的導入，其挑戰更勝於全新的系統導入過程。
個案公司在以不影響生產活動，且維持晶圓廠每月滿載產能運轉的情況下導入資料倉儲系統，來滿足生產效率的提升需求。因此，系統導入方法的選定是相當重要的。本研究透過成功導入資料倉儲的案例探討與分析，並從中獲得理論與實務之交互印證，進一步分析出適配模式來制定資料倉儲建置計劃，其中，以資料萃取轉置處理、超資料管理機制之建構應用為主要改善重點。再配合晶圓廠生產管理作業需求，根據使用者需求的急迫程度，利用循環模式規劃出建置流程階段，逐步提升資料倉儲的資料覆蓋率來滿足使用者需求。
本研究所完成的具體成果，是以資料倉儲為核心基礎，建構雙軌制後端資料處理流程，包含資料模型設計以及資料萃取、轉換與載入等，並根據彙整之使用者應用需求，著手設計整合性資料分析平台。同時，透過超資料管理機制的規劃，具體提升資訊透明度，來優化資料倉儲的應用價值。在前端使用者介面的設計部份，則著墨在如何應用線上分析處理技術，提出資訊如何呈現的建議。整體導入計劃中的細部流程，以需求分析為始，完成整體系統分析，提出系統建構發展策略後，設計出資料模型與實體系統架構，包含資料萃取轉置機制與超資料管理、以及前端使用者介面設計評估，最後完成資料倉儲系統發展藍圖與導入階段的制定。本研究所完成之導入規劃將作為個案公司實務建置之參考依據。

According to improve the performance of business operation and administration, more and more companies try to use system solutions to explain overall operation status information. With the enhancement in technique and theory, Data Warehouse becomes a very key player in this upcoming market. This upsurge is also impact the FAB manufacture business. However, the resource to implement Data Warehouse is truly big investment. And all of implementation step need to be completed within a factory running by full capacity. The challenge is more difficult to complete a brand new installation.
In this case, the capacity of daily operation is not allow to be impacted. It is very impartment and difficult to figure out a methodology to execute the implementation without interfere the operation and also fulfill the desire of efficiency improvement. We try to approach from understand the daily operation and management process inside FAB and also study some success stories in other firm. We try to construct a best-fit solution that could be cross-verified by theory and real practices. The circulation pattern is used to build up the implementation flow. According to the priority of user’s requirements, we could set up a phase plan to implement. Through proceed key data scope one by one; we could increase the data coverage ratio to fulfill user’s need. The detail implementation steps are designed by through user request interview, system analysis and solution evaluating. The data model architecture and system architecture could be designed by bringing all of these outputs together. This architecture is also including data transformation management, metadata management and front-end user interface design. Then, we have a fully completed plan for data warehouse implementation.
In this research, we build up an integrated analysis platform to support end users retrieve information on daily job. The core of this platform is based on a clear and well-defined data warehouse. The process includes data definition and retrieving, data transferring and transformation and data model define and design. At the same time, we also provide a mechanism to manage meta-data and provide a clearer view of information. All of those processes focus on nothing but one thing, to increase the add-on value of data ware house. The technique of on-line web base analysis behavior support is the key point of front-end design. It will be a great help to present information to be an accuracy advisement by using a user friendly operating flow and view.

[英文文獻]
A. Berson and S. J. Smith, 1997, Data Warehousing, Data Mining, & OLAP. McGraw-Hill.
B. P. Basaran, 2005, A Comparison of Data Warehouse Design Models, Atilim University.
J. Han and M. Kamber, 2006, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers.
M. Rifaie, K. Kianmehr, R. Alhajj, M. J. Ridley, 2008, Data Warehouse Architecture and Design, School of Informatics, Bradford University.
N. M. Dar, N. Zahra, 2009, Generic Metadata Repository for a Data Warehouse, Islamic International University.
P. M. Lewis, A. Bernstein, M. Kifer., 2002, Databases and transaction processing : an application-oriented approach, Addison-Wesley.
R. Elmasri, S. B. Navathe, 2004, Fundamentals of Database Systems, Addison-Wesley.
R. K. Becker, 1998, The Data Warehouse Lifecyclcle Toolkit second edition. Wiley Publishing, Inc.
S. Saroop & M. Kumar, 2011, Comparison of Data Warehouse Design Approaches from User Requirement to Conceptual Model: A Survey, GGSIP University.
W.H. Inmon, 1996, Building the Data Warehouse, John Wiley & Sons, Inc.
[中文文獻]
王毅，2010，數據倉庫建模和設計的最新進展，電腦開發與應用第23卷第3期.
吳毓娟、皮世明，2006，建置資料倉儲專案管理之個案研究─以IT部門為角度，開南大學專案管理研討會論文集(二).
李志宏，2006，架構式資料倉儲之研究，國立中山大學資訊管理學系（研究所）.
李卓翰，2006，資料倉儲理論與實務，學貫行銷.
沈兆陽，2001，資料倉儲與Analysis Services--SQL Server 2000 OLAP解決方案，文魁資訊.
林培倫，2005，應用「線上分析處理」技術建構客製化模板分析工具之研究－以大學課程資源網光纖科目為例，國立臺灣師範大學工業教育學系（研究所）.
林傑斌、卓彰賢、張一岑，2006，資料倉儲理論、設計與應用，網奕資訊科技股份有限公司.
林傑斌、張一岑、張太平，2006，資料倉儲與資料採擷，博碩文化股份有限公司.
林裕仁，1999，資料倉儲應用實例之建置與系統效能之研究，國立屏東科技大學資訊管理系（碩士班）.
梁定澎，2006，決策支援系統與企業智慧，智勝文化.
郭和偉、孫德寶、韓鋒、秦元慶、皮鯉，2004，數據倉庫實現過程及在線分析，計算機與應用化學第21卷第2期.
黃舒郁、謝銘智、蕭釧瑛、林永青，2010，淺談資料倉儲，中興工程季刊第109期 (p63-71).
謝銘智、蕭釧瑛、黃舒郁、林永青，2011，資料倉儲建置實務－以環境品質資料為例，中興工程季刊第111期 (p23-34).
藍中賢、吳郁瑩譯 (R. Elmasri, S. B. Navathe著)，2005，資料庫系統原理，台灣培生教育出版股份有限公司.
[網路文獻]
B. Inmon, 1998, The Operation Data Store, Retrieved 2012/1/10, from http://www.information-management.com/issues/19980701/469-1.html.
IBM, 1998, Data Modeling Techniques for Data Warehousing, International Business Machines Corp.. Retrieved 2011/10/26, from IBM Redbook: http://www.redbooks.ibm.com/redbooks/pdfs/sg242238.pdf.
V. Rainardi, 2008, Building a Data Warehouse with samples in SQL server, Retrieved 2011/12/2, from http://www.apress.com .
Wikipedia, 2011, 線上分析處理, Retrieved 2011/10/26, from http://zh.wikipedia.org/wiki/%E7%B7%9A%E4%B8%8A%E5%88%86%E6%9E%90%E8%99%95%E7%90%86.
李宗翰，2004，資料倉儲的類型，Retrieved 2011/11/11, from http://www.ithome.com.tw/itadm/article.php?c=28914.
個案公司首頁，2003，Retrieved 2011/11/1, from Inotera: http://ir.inotera.com/default_TW.asp.
黃旭愉，2005，決策分析的利器—線上分析處理(OLAP)，Retrieved 2011/12/01, from http://campaign.hncb.com.tw/intranet/monthly/mon032/03202.pdf.
楊惠芬，2007，資料倉儲應用將從後臺延伸到第一線使用者，Retrieved 2011/12/20, from iThome online: http://www.ithome.com.tw/itadm/article.php?c=46200.
鍾峰宜，2004，資料倉儲結構設計簡介，Retrieved 2012/1/15, from 倍力資訊股份有限公司: http://www.mpinfo.com.tw/TechnologyColumnFiles/OOAD_A_200409.pdf.