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.

[1] 彭森明, 高等教育校務研究的理念與應用, 2013
[2] 彭森明, 大學生學習成果評量：理論、實務與應用, 2010
[3] 羅孟彥, 支援教育品質管理之整合式資訊系統建構與研究, 教育科學研究期刊58卷4期 頁69-101, 2013 Dec.1
[4] 曾元顯, 校務研究資料庫的建構與分析應用, 當代教育研究季刊24卷1期頁107-134, 2016 Mar
[5] Han J. and Kamber M., “Data Mining: Concepts and Techniques”, 2000
[6] Nilakanta S., Scheibe K. and Rai A., “Dimensional issues in agricultural data warehouse designs.”, 2007
[7] Hibernate, https://en.wikipedia.org/wiki/Hibernate_(framework)#History
[8] Hibernate ORM, http://hibernate.org/orm/
[9] Hibernate HQL, https://docs.jboss.org/hibernate/orm/3.3/reference/en/html/queryhql.html
[10] Rahm E. and Bernstein P., “A survey of approaches to automatic schema matching.”, 2001
[11] Tomcat, https://zh.wikipedia.org/wiki/Apache_Tomcat
[12] Tomcat Architecture, https://www.jvmhost.com/tomcat-hosting/
[13] R Kimball, J Caserta, “The Data Warehouse? ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data”, 2011
[14] W.H. Inmon, “Bulding the Data Warehouse 3 Edition”, 2002
[15] Nadeem Iftikhar and Torben Bach Pedersen, “Schema Design Alternatives for Multi-granular Data Warehousing”, 2010
[16] Jiawei Han, Micheline Kamber and Jian Pei, “Data Mining: Concepts and Techniques, 3rd edition”, 2011
[17] Ralph Kimball, Margy Ross, The Data Warehousing Toolkit., 2002
[18] Java RMI, https://docs.oracle.com/javase/tutorial/rmi/overview.html
[19] Mysql, https://en.wikipedia.org/wiki/MySQL
[20] PostgreSql, https://en.wikipedia.org/wiki/PostgreSQL
[21] PostgreSql Users, https://www.postgresql.org/about/users/