English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 93218/123590 (75%)
Visitors : 27758318      Online Users : 590
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/69112
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/69112

    Title: A Recommendation Mechanism on OLAP Operations Based on Social Network
    Other Titles: 整合社群關係的 OLAP 操作推薦機制
    Authors: 陳信固;李蔡彥
    Contributors: 資科系
    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: 2012-05
    Issue Date: 2014-08-21 11:04:02 (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.
    Relation: Appeared in Proceeding of the 2012 International Conference on Information Management, 2012.
    Data Type: conference
    Appears in Collections:[資訊科學系] 會議論文

    Files in This Item:

    File Description SizeFormat
    201205.pdf1581KbAdobe PDF627View/Open

    All items in 政大典藏 are protected by copyright, with all rights reserved.

    社群 sharing

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback