在數位科技快速成長的時代，已有許多的演算法及技術被提出來加以探究，並且在每天大量產生的電子資料中去分析以探勘出有意義的資訊，以提升數位圖書館知識分享及服務品質。在本研究中，我們建構一個研究模型，以達到個人化服務及數位圖書館管理的目的。以個人化服務為例，即是利用相似特性，如身份別相同之讀者的歷史借閱館藏記錄以產生關聯法則，做為館藏推薦的基礎。除此之外，我們還運用OLAP的技術來分析資料方塊，而這些資料方塊是以具有相關聯性的電子資料所產生而來，以獲得使用數位圖書館資源的最新資訊，並有效率地提升數位圖書館的資源管理。我們針對所提出的研究方法以幾個例子加以分析及討論，而數位圖書館的管理人員便可以我們所建議的研究方法在有限的預算中，採購核心及熱門的館藏，以維持及滿足讀者的需求。 In the era of digital technology growing rapidly, a lot of algorithms and techniques have been proposed to explore and analyze the large quantities of electronic data produced everyday to discover meaningful information so as to enhance the knowledge sharing and service quality of digital library. In this study, we design a system model to enable personalized services and management on digital library. For personalized services, association rules discovered from the books borrowed by the readers in the same cluster are used as the basis of book recommendation. Besides, we apply the OLAP technology to analyze the data cubes, which are created by the relevant electronic data, to obtain the desired up-to-date information for resource usage to effectively promote the resource management of digital library. In this study, several application cases for the proposed approach were analyzed and discussed. Based on the proposed approach, the library managers are expected to purchase the core and hot books in limited budget to maintain and satisfy the requirements of readers.
