臺灣博碩士論文加值系統

隨著人們對資料庫使用的需求增加，使用者對資料的查詢方法也越來越多樣，促使近年來偏好查詢成為一個很熱門的研究議題。在所有的查詢中，Skyline查詢更是在現今資料庫以及資料檢索中熱門的研究題目。伴隨著科技的演進，人們可以收集和利用的資料急劇增長，巨量資料的運算處理變成迫切的問題。藉由Google在2004年發表的一份開放文件中分享了MapReduce程式化運算框架，以往許多查詢在巨量資料環境遇到的障礙都得到有效的解決方案。
Skyline查詢是一件高時間複雜度的工作，面臨到巨量資料時的處理更是困難，因此近年來對於Skyline在巨量資料查詢的研究也逐漸熱絡發展。本研究目的在於如何設計更有效的MapReduce演算法使得Skyline查詢處理能夠更有效進行，對此演算法進行詳細的說明，最後在Hadoop平台上實作並驗證此演算法具有更佳的有效性及可用性。

With the increasing number of querying methods, preference queries become a very popular research topic. Among all kinds of queries, skyline query is important in today's databases and information retrieval. Moreover, the development of technologies makes it possible to collect and utilize the rapid growth of data. Google in 2004 published an open document to share a computing framework named MapReduce, which makes big data processing efficient.
Skyline query costs much in processing, and it becomes even more difficult when facing a huge amount of data. In this study, we designed an efficient MapReduce algorithm for skyline queries. We also implemented the algorithm on the Hadoop platform to verify the efficiency and effectiveness of this algorithm.

第1章 緒論 1
第2章 相關研究 4
2.1 Skyline演算法的相關研究 4
2.2 Skyline在高度分散環境下查詢處理的相關研究 5
2.3 MapReduce框架 8
2.4 適用於MapReduce框架的Skyline查詢的相關研究 10
第3章 問題與定義 12
3.1 問題 12
3.2 直觀演算法 13
3.3 問題定義 16
第4章 資料分割及演算法介紹 18
4.1 演算法概要 18
4.2 網格分割及篩選 20
4.3 角度分割及篩選 22
4.4 兩種分割方法的效能分析 25
第5章 實驗與結果 27
5.1 回應時間 27
5.2 片段運算平衡 28
5.3 片段Global Skyline的貢獻比較 29
第6章 結論 31
參考文獻 32

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