English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51510/86705 (59%)
Visitors : 8271137      Online Users : 140
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU 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
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/111021


    Title: 以決策樹為基礎之企業雲端服務推薦
    Other Titles: A decision-tree based recommending approach for enterprise cloud service
    Authors: 林子能;Lin, Zih-Neng
    Contributors: 淡江大學企業管理學系碩士班
    張瑋倫
    Keywords: 雲端;產品推薦;機器學習;C4.5;Cloud;product recommending;mechine learning
    Date: 2016
    Issue Date: 2017-08-24 23:41:54 (UTC+8)
    Abstract: 根據美國電信公司XO Communications預測,2017年將會有86%的企業建置混合雲。在技術面與實務面皆顯示,企業使用混合雲部署的比例有著顯著提升,而混合雲則是企業目前最愛用的一項部署方式,兼具公有雲的好處以及私有雲的優點,使得企業多是以混合雲方式來部屬企業的雲端服務。而近年來經濟不景氣,使企業更關注雲端運算的低廉與彈性優勢,也促使越來越多資訊與通訊公司,加入提供雲端運算服務的行列。本研究有鑒於選擇不適當雲端專案的高失敗率,以及選擇因素多樣複雜等問題,提出兩個研究目的,首先,透過過去文獻與專家篩選出雲端服務關鍵決策因子,並利用機器學習方式建立有效之決策模式。
    本研究採用文獻探討尋找決策關鍵因子,並藉由專家前測問卷測試題目信度,再行發放專家問卷及企業使用者問卷,將企業使用者問卷收集並將數據帶入C4.5演算法得到一決策樹模型,經由指標驗證,確認決策樹的預測能力。本研究結果發現,透過專家學者之專家效度,驗證本研究經由文獻所找出的17項關鍵決策因子,結果顯示專家學者皆認同其重要程度。此外,在決策樹中建立了35條規則,並發現台灣企業常見的雲端產品使用為Amazon AWS EC2服務與IBM Ssmart Cloud,與不同產業之間所使用的雲端產品為何,如教育業較多使用IBM Smart Cloud,進一步找到選擇不同雲端產品時所關注的關鍵選擇因子,如35條規則有28條具有資訊安全能力的屬性。透過本研究決策樹的規則,可以清楚根據不同的需求屬性,推薦出適合的雲端產品,並在決策樹驗證之後,其精準度有85%及回想率有86%之高水準表現,再與過去相類似方法的研究相比之下,本研究決策樹模型的預測相當具有一定程度效用。
    According to the prediction of XO Communications, there will be 86% of businesses building hybrid cloud in 2017. Both in technology and practice, The proportion that enterprises use hybrid cloud for deployment has been significantly improved in terms of technical and practical perspectives. In addition, hybrid cloud is one of the deployment ways enterprises prefer to use currently since it has the benefits of public cloud and private cloud simultaneously. That is, most enterprises utilize their cloud services by hybrid cloud. Recently, economic depression makes enterprises more concern about the low cost and flexibility of cloud applications. More and more companies provide cloud computing services. This research proposed two research goals in order to take into account the high failure rate of selecting inadequate cloud projects as well as diverse and complex factor selection. First, we identify key factors of selecting cloud services through literature and experts. Besides, we use machine learning technique (decision tree) to establish an effective decision model.
    In this study, we surveyed literature to identify key factors and inquired experts to validate the designed questionnaire. The questionnaires for business users are collected for C4.5 algorithm to obtain a decision model. In the study, experts confirmed content validity of selected 17 key factors through literature. In addition, 35 rules were established in the decision tree and result showed the common cloud products used in Taiwan were Amazon AWS EC2 and IBM Smart Cloud. In particular, IBM Smart Cloud was used in education business mostly. Furthermore, we discovered the key factors for selecting different cloud products. For example, there are 28 with the properties of information security capabilities. Appropriate cloud products can be recommended according to different needs. The accuracy of our model is 85% and the recall rate is 86% of the decision tree, which achieves the high standard of performance. Compared to the past researches with similar methods, the prediction of the decision tree model is effective for cloud solution selection in practice.
    Appears in Collections:[企業管理學系暨研究所] 學位論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML18View/Open

    All items in 機構典藏 are protected by copyright, with all rights reserved.


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