在短短數年之內，網際網路的使用人口幾乎呈爆炸性地增加，全球網站越來越多，頻寬壓力造成的網路塞車也日益嚴重，消費者已經鮮有耐性等待漫長的查詢而且最後還看到一堆沒有用的資料( 或不是你想要的資料)，因此推播技術（Push Technology）應運而生。

　　雖然目前的推播技術可以把第一手的資訊，即時的送給使用者，但追根究底這推播的資料真的是使用者需要的嗎？或者仍然只是推播一堆沒有用的資料給使用者呢？

　　本論文提出一個利用資料採礦（Data Mining）技術，並使用行動代理者（Mobile Agent）來延伸推播技術之方法，使推播技術在長遠之情況下能提供使用者更需要的文件，並能瞭解使用者對所接受之文件興趣的動向，以便做為將來判斷推播文件的方向及改善系統效能的依據，它讓使用者不需再花費許多時間及金錢在搜尋資料上，並且能有效的減少頻寬的使用以及推播伺服器端的負擔。

　　本系統是以MACE（Mobile Agent Carrier Environment）作為行動代理者的運載環境，因此所有的架構是依MACE的Agentlet-Serverlet架構來實現，再加上推播技術使之可運用行動代理者來收集使用者的資料、興趣、閱讀行為，並利用資料採礦的方法論，進而分析使用者的行為記錄，用以改善推播文件系統、預測使用者的興趣所在，成為智慧型的代理者推播文件系統。

　　Recently, the population of Internet increases explosively. There are more and more websites and the bandwidth becomes more and more insufficient day by day. Users are loosing their patience waiting for searching and eventually getting a lot of useless information. Therefore Push Technology emerged.

　　Presently, Push Technology can give timely information to users. But are these information really what users need? Or, it just pushes more useless data to users?

　　This thesis proposes a framework, which uses Data Mining Technologies and Mobile Agent to extend Push Technology. The propose is to let Push Technology fulfill what users need in the long term. It collects user’s behavior when they are reading document, and predicts future direction of pushed documents. According to the collected information, we can improve system performance. This system will enable users to save time and money on searching information. It will also reduce the usage of bandwidth and the load of the information server.

　　The implementation of a prototype system is based on the MACE [WAN2] (Mobile Agent Carrier Environment). Consequently, the whole skeleton is designed according to the structure of the Agentlet-Serverlet protocol of MACE. In addition of Push Technology, this system can use Mobile Agent to gather users' information, interest, reading behavior, and apply the methodology of Data Mining to analyze the record of user’s behavior to modify the Push system. Predict what users are interested in to become an intelligent Mobile Agents for Interactive Push Technology.

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