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    政大機構典藏 > 理學院 > 資訊科學系 > 學位論文 >  Item 140.119/54334
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/54334


    Title: 整合社群關係的OLAP操作推薦機制
    A Recommendation Mechanism on OLAP Operations based on Social Network
    Authors: 陳信固
    Chen, Hsin Ku
    Contributors: 李蔡彥
    Li, Tsai Yen
    陳信固
    Chen, Hsin Ku
    Keywords: 社群網路分析
    推薦機制
    社群偵測
    商業智慧
    網絡中心性
    Social Network Analysis
    Recommendation Mechanism
    Community Detection
    Business Intelligence
    Network Centrality
    Date: 2011
    Issue Date: 2012-10-30 10:44:43 (UTC+8)
    Abstract: 近幾年在金融風暴及全球競爭等影響下,企業紛紛導入商業智慧平台,提供管理階層可簡易且快速的分析各種可量化管理的關鍵指標。但在後續的推廣上,經常會因商業智慧系統提供的資訊過於豐富,造成使用者在學習階段無法有效的取得所需資訊,導致商業智慧無法發揮預期效果。本論文以使用者在商業智慧平台上的操作相似度進行分析,建立相對於實體部門的凝聚子群,且用中心性計算各節點的關聯加權,整合至所設計的推薦機制,用以提升商業智慧平台成功導入的機率。經模擬實驗的證實,在推薦機制中考慮此因素會較原始的推薦機制擁有更高的精確度。
    In recent years, enterprises are facing financial turmoil, global competition, and shortened business cycle. Under these influences, enterprises usually implement the Business Intelligence platform to help managers get the key indicators of business management quickly and easily. In the promotion stage of such Business Intelligence platforms, users usually give up using the system due to huge amount of information provided by the BI platform. They cannot intuitively obtain the required information in the early stage when they use the system. In this study, we analyze the similarity of users’ operations on the BI platform and try to establish cohesive subgroups in the corresponding organization. In addition, we also integrate the associated weighting factor calculated from the centrality measures into the recommendation mechanism to increase the probability of successful uses of BI platform. From our simulation experiments, we find that the recommendation accuracies are higher when we add the clustering result and the associated weighting factor into the recommendation mechanism.
    Reference: 參考文獻
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    Description: 碩士
    國立政治大學
    資訊科學學系
    97971014
    100
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0097971014
    Data Type: thesis
    Appears in Collections:[資訊科學系] 學位論文

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