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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/102566


    Title: An adaptive learning object management and search mechanism based on time-series mining
    Other Titles: 基於時間序列探勘之適性化數位學習元件管理暨檢索機制
    Authors: 嚴昱文;Yen, Yu-Wen
    Contributors: 淡江大學資訊工程學系博士班
    趙榮耀
    Keywords: 使用者生成資料;資料探勘;資訊檢索;時間序列;社群網路分析;數位學習;User-generated data;data mining;Information Retrieval;Time-series;Social Network Analysis;E-Learning
    Date: 2014
    Issue Date: 2015-05-04 09:59:03 (UTC+8)
    Abstract: 近年來,資訊科技的蓬勃發展促使網際網路(World Wide Web)變成了互動的平台。雖然互動的參與者,尤指使用者與其相關的事件,在各個方面皆彼此相異,但我們很確定地可以預見大量且複雜的資訊量。 這個現象的確造成了在資訊管理、取得以及重複使用上的困難,同時也降低了這些資訊本身的價值。在本論文中,我們嘗試提出有效的方法來管理使用者生成資料(User-generated Data)與其衍生之資訊,更試著藉以經驗來實作使用者中心的服務。
    本論文著重於有意義的管理與重複使用使用者生成資料,尤其是其對於數位學習活動進行時之支援。首先,我們提出了一套用以管理使用者生成資料的狀態機,它主要用以明確地記錄此類資料相互間的關係,以及其衍生資訊間之關係。為了增加資料模型的準確度,我們再狀態機的設計之上,提出了一套時間序列的探勘演算法,用以針對特定時間區段內的資料之互動,進行處理。最後,在此基礎之上,我們實作了一套資料庫管理系統及資料檢索服務,以簡化使用者於數位學習資源檢索時之複雜度。我們蒐集了500位使用者在過去五年中於其使用之社群媒體(如Facebook, Twitter等)所創建出的數據,並用以進行效能與可行性之評量。實驗結果證實,本研究所提出之資料處理方法暨檢索服務,能有效支援數位學習活動中,資訊檢索之複雜度。
    Recent advances in information technology have turned out World Wide Web to be the main platform for interactions where participants – users and corresponding events – are triggered. Although the participants vary in accordance with scenarios, a considerable size of data will be generated. This phenomenon indeed causes the complexity in information retrieval, management, and reuse, and meanwhile, turns down the value of this data. In this thesis, we attempt to achieve efficient management of user-generated data and its derivative contexts for human supports.
    This thesis concentrates on the meaningful reuse of user-generated data, especially its usage for learning purpose, through an efficient and purpose-built data management process. First, an intelligent state machine, which is the essence to the scenario of user-generated data processing, was developed to identify, especially those frequently-accessed and with timely manner, relations of data and its derivative contexts. To accelerate the accuracy in data correlation modeling, a temporal mining algorithm is then defined. This algorithm is applied to highlight the event that a data item is being accessed, and further examines its relative attributes with other correlated items. Last, but not the least, we present a conceptual scenario of human-centric search to demonstrate the proposed approach. The performance and feasibility can be revealed by the experiments that were conducted on the data collected from open social networks (e.g., Facebook, Twitter, etc.) in the past few years with size around 500 users and 8,000,000 shared contents from them.
    Appears in Collections:[資訊工程學系暨研究所] 學位論文

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