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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/18856

    Title: Using Taxonomic Indexing Trees to Efficiently Retrieve SCORM-compliant Documents in e-Learning Grids
    Authors: 時文中;Shih, Wen-Chung;曾憲雄;Tseng, Shian-Shyong;楊朝棟;Yang, Chao-Tung
    Contributors: 資訊多媒體應用學系
    Keywords: e-Learning, SCORM, Grid computing, Globus Toolkit, Information retrieval
    Date: 2008
    Issue Date: 2012-11-26 15:09:43 (UTC+8)
    Abstract: With the flourishing development of e-Learning, more and more SCORM-compliant teaching materials are
    developed by institutes and individuals in different sites. In addition, the e-Learning grid is emerging as an
    infrastructure to enhance traditional e-Learning systems. Therefore, information retrieval schemes supporting
    SCORM-compliant documents on grid environments are gaining its importance. To minimize the query
    processing time and content transmission time, our idea is to use a bottom-up approach to reorganize documents
    in these sites based on their metadata, and to manage these contents in a centralized manner. In this paper, we
    design an indexing structure named Taxonomic Indexing Trees (TI-trees). A TI-tree is a taxonomic structure and
    has two novel features: 1) reorganizing documents according to the Classification metadata such that queries by
    classes can be processed efficiently and 2) indexing dispersedly stored documents in a centralized manner which
    is suitable for common grid middleware. This approach is composed of a Construction phase and a Search
    phase. In the former, a local TI-tree is built from each Learning Object Repository. Then, all local TI-trees are
    merged into a global TI-tree. In the latter, a Grid Portal processes queries and presents results with estimated
    transmission time to users. Experimental results show that the proposed approach can efficiently retrieve
    SCORM-compliant documents with good scalability.
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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