臺灣博碩士論文加值系統

校務研究在國外已經運行多年，而台灣近幾年由於教育政策與社會環境的改變，也開始在各個高等教育機構中推行校務研究，讓決策者可以根據各面向分析的結果做出正確的決策，使得高等教育機構做出成功的轉變以應對時代的變遷。
校務研究所涵蓋的範圍廣泛，所需蒐集的資料分散於各個高等教育機構中的行政單位，而各個行政單位為了各自的需求而設計了多樣化的系統，導致各單位在資料庫系統的選擇與資料儲存的設計差異極大，進而造成校務研究資料蒐集上的困難。
為了要整合這些存在於不同資料庫系統與設計的資料，本研究採用支援多種資料庫連線的軟體框架將所有的資料由這些異質資料庫中導入本系統，並自行撰寫資料轉換程式與聚合運算函式，將資料轉換成分析使用的資料模型，然後由目前世界上通用度最廣的Http通訊協定予以使用者連結介面來操作資料模型，以獲得使用者所需要的結果。

Institutional Research has been operated for several years in other countries, in Taiwan, due to the changes in education policy and society environment, higher education institutions are starting to implement Institutional Research. According to multiple directional analyses in Institutional Research, decision maker in higher education institutions can make correct decisions which lead to a successful transformation to accommodate this changing age.
Institutional Research covered a wide range of issues, data which it need are spread in multiple affairs in high education institutions. However, each affair own their system, these system’s designers choose various database and design different database schema, above two reasons, are barriers to collect the data which Institutional Research need.
In order to intergrade these different databases and database schemas, we propose an architecture which combine a database connection software framework, this software framework support various databases connection and transfer data to our system. Also we design a program to transform data to data model, and provide Http protocol, the widest used protocol in the world, interface which user can manipulate data model to acquire the result user need.

摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
一、 緒論 1
1-1研究背景 1
1-2研究動機與目的 2
1-3章節介紹 3
二、 文獻探討 4
2-1 資料倉儲Data Warehouse 4
2-2 Hibernate ORM 6
2-3 Schema Matching 7
三、 系統架構 9
3-1軟體框架以及其流程 9
3-2 Mysql 11
3-3 PostgreSql 11
3-4 Tomcat 12
3-5 ETL 13
3-6 OLAP Cube 14
3-7 Request Handler 15
3-8 Data Center 15
四、 研究方法 16
4-1資料前處理 16
4-2 ETL 19
4-3 OLAP Cube 21
4-4 Request Handler 23
4-5 網頁操作介面 25
五、 實驗 26
5-1 資料從資料庫載入的消耗時間 27
5-2 原始資料轉換為資料模型的消耗時間 28
5-3 總耗時 29
5-4資料載入時間與資料轉換時間比較 30
5-5資料載入與轉換的記憶體使用量 31
六、 結論 33
參考文獻 34

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[20] PostgreSql, https://en.wikipedia.org/wiki/PostgreSQL
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