臺灣博碩士論文加值系統

大量資料中潛藏著有用的特徵或知識。若能妥善的進行資料分析與探勘，挖掘出這些隱藏的特徵，可以提供主管，做為決策的重要依據。為了讓非電腦專業的資料分析人員，容易地進行探索式資料分析，幫助使用者從不同的角度和多個抽象階層觀察分析結果，系統必須具有高度的親和力。
本研究以屬性階層及擴充式查詢語言為基礎，提出一個圖形化資料分析模式來支援非結構性的沙盤推演式分析。其中屬性階層扮演非常重要的基礎，它代表資料分析的領域背景知識。配合屬性階層可以進行資料一般化，以進行高階的查詢與分析。本研究模式提供交互式的分析環境，親和性高的操作介面，讓非電腦專業的分析人員可以根據不同的屬性維度上探下探和彙總分析，以便容易地進行沙盤推演式的資料分析。

Useful patterns or knowledge may hide in huge amount of data. By properly analyzing and mining the data, an analyst can uncover and provide useful information to executives for decision making. In order to facilitate data exploration task to those data analysts who are not computer professionals and help user explore data from different angles and at multiple abstraction levels, a system has to be user-friendly.
This study proposed a graphical data analysis model based on attribute hierarchy and an extended relational query language. The model is capable of supporting unstructured what-if type data analysis. Attribute hierarchies that store domain knowledge play an important role during data analysis sessions. Generalization can be performed according to attribute hierarchies for high level queries and analysis. The proposed model provides an interactive data analysis environment for data analysts who are not computer profes-sionals to perform drill-down, roll-up and summarization along any attribute dimensions and to perform what-if data analysis.

一、緒論1
1.1研究動機1
1.2研究目的2
1.3論文架構3
二、問題描述4
2.1 結構化查詢語言的限制4
2.2查詢語言的學習障礙5
三、文獻探討7
3.1背景知識7
3.1.1人機互動之溝通樣式7
3.1.2資訊視覺化探索10
3.1.3線上分析處理13
3.2相關研究18
3.2.1學術界的視覺化探勘研究18
3.2.2實務界的視覺化資料分析22
四、視覺化資料分析26
4.1系統架構26
4.2屬性階層27
4.1.1屬性階層結構27
4.2.2屬性一般化29
4.2.3屬性階層建置32
4.3視覺化資料分析模式33
4.3.1視覺化分析模式33
4.3.2資料分析38
4.4支援資料分析功能41
4.5視覺化技術比較46
五、視覺化資料分析系統雛形48
5.1雛形環境及實驗資料48
5.2雛形介面及功能49
5.3操作流程50
六、結論56
參考文獻58
簡 歷60

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