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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/8432

    Title: 支援使用者觀點之線上分析系統
    Authors: 郭俐蘭;Li-Lian Guo
    Contributors: 資訊工程研究所
    Keywords: 線上分析系統;使用者觀點
    Date: 2000-07-04
    Issue Date: 2009-09-22 11:26:49 (UTC+8)
    Publisher: 國立中央大學圖書館
    Abstract: 系統Log登錄了使用者操作系統的紀錄,藉由分析Log資料,可以得到許多寶貴的資訊,包括系統的使用效能、使用者的使用習性、和興趣等,這些資訊提供給系統設計者可以提供改進系統的資訊,提供給系統的擁有者,可以幫助了解系統使用者,進而制定適宜的對策來增進互動。 許多分析Log紀錄的系統或技術都是建基於資料的觀點,而事實上進行資料分析的使用者才是真正決定分析資料為何的人,由資料觀點來擷取資訊,如果系統內沒有使用者需要的資料內容,往往使用者需要對資料進行再行整理與運算,這無疑增加了使用者負擔。 所以我們設計了一套線上分析系統,以支援使用者觀點的方式來分析資料。首先,利用切割處理,讓使用者根據分析需求定義出各種資料特徵,賦予資料較直覺且易理解的意義。這個部分,我們提供各種切割函式,讓使用者依據切割方法將資料特徵模組化,借以提升資料特徵意義的可信度。再來,透過取代處理,建構資料特徵空間,以獲得特徵相關資料。另外,系統還提供布林運算,讓使用者可以以多重條件定義資料特徵,或是建構資料特徵空間。藉由訂定資料特徵的方式,讓使用者可以結合自己的觀點到Cube的資料中,直接獲取所需資料。 有了這些資料後,再來就是對資料進行分析的處理,分析的結果則是包含這些資料特徵意義。分析的方法除了資料探勘技術外,我們還考慮使用統計的方法,因為統計在資料驗證方面的能力十分受肯定,藉由統計的方法對資料進行考驗分析,以增加分析結果的可信度。 System log data includes the records of system users’ operation. By means of analyzing log data, we can get much valuable information about system efficiency, users’ habitual behaviors and interests, etc. This information is useful to realize system users more, even to help set up proper strategies. Many systems and technologies of analyzing log records are based on viewpoint of data. However, who needs to analyze data is who should decide the content of data for analysis. Analyzing log records based on viewpoint of data, if the data users required doesn’t exist in systems, usually increases users’ loads by data rearrangement and recalculation. Hence we design an on-line analytical processing system based on viewpoints of system users to analyze data. First, by operating the split process, users can define various kinds of data features to give more sensible meanings for cube data. In this part, we offer several split functions. Users can model data features by these functions and increase credibility of data feature definitions. And then operating replacing process, users are able to construct feature space to obtain feature-related data. Moreover, the system provides Boolean operation for users. So users can consider multiple conditions to define features or construct feature space. Through data features, users combine their viewpoints with cube data and get required data directly. After gaining data, we want to do some analysis. The analytical results will contain these feature definitions. Besides data mining technologies, we also adopt statistical methods for data analysis. People have affirmed the test ability of statistics. By using some statistics methods, users will put more trust in analytical results.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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