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研究生: 陳家慶
Chen, Chia Ching
論文名稱: 以MapReduce做有效率的天際線查詢
Efficient Skyline Computation with MapReduce
指導教授: 陳良弼
Chen, Arbee L.P.
學位類別: 碩士
Master
系所名稱: 理學院 - 資訊科學系
論文出版年: 2013
畢業學年度: 102
語文別: 中文
論文頁數: 34
中文關鍵詞: 巨量資料天際線
外文關鍵詞: Skyline, MapReduce
相關次數: 點閱:230下載:6
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  • 隨著巨量資料的議題逐漸被重視,有越來越多的巨量資料的分析都利用MapReduce作計算處理。而在資料庫查詢中,天際線查詢是一種常見的決策分析方法,其目的是要幫助使用者找出資料庫中各維度的數值貼近使用者查詢條件的資料。然而,過去在大量資料的查詢方法中,如果資料筆數較多,同時查詢的維度也大的情況下,往往會有著效率不彰的問題。因此,本研究提出一種在大量資料中,有效率應用MapReduce作天際線查詢的方法。而根據實驗結果顯示,我們的方法,比先前方法更有效率。


    With the big data issue being taken seriously today, more and more big data is processed with MapReduce. Moreover, skyline query is a common method for decision making, which helps users find the data whose value in each dimension is close to the user query. In the past, if the data is huge, or the data space involves many dimensions, the query processing becomes inefficient. Therefore, in this study, we present a new method to process skyline queries with MapReduce. According to the experimental results, our method is more efficient than previous methods.

    第1章 緒論 1
    第2章 相關研究 3
    2.1 天際線查詢 3
    2.2 平行天際線查詢 4
    2.3 MapReduce 5
    2.4 Skyline在MapReduce的演算法 6
    第3章 問題定義 7
    第4章 MR-Sketch演算法 8
    4.1 資料過濾階段 9
    4.2 外部切割階段 11
    4.21 完全配對分割法(All-Pair Partitioning) 11
    4.22 中間值切割法Middle Split Partition 14
    4.23 Key數與資料傳輸係數 16
    4.24 維度切割的選擇 19
    4.3 內部切割階段 20
    第5章 實驗結果 23
    5.1 資料型態 23
    5.2 實驗流程 25
    5.3 維度大小與資料筆數的影響 26
    5.4 取樣點數對於實驗結果的影響 29
    5.5 外部分割最大的分裂維度對實驗的影響 30
    第6章 結論 32
    參考文獻 33

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    [15] L. Chen, K. Hwang, and W. Jian, “MapReduce Skyline Query Processing with a New Angular Partitioning Approach," in Proceedings of the Parallel and Distributed Processing Symposium Workshops & PhD Forum, 2012.

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