臺灣博碩士論文加值系統

天際線查詢(skyline query)是一種高效率多準則決策的資料分析工具，並且在資料庫的領域中受到相當多的關注，且將天際線查詢納為基本語法之一。在多核心處理器的發展潮流下，我們提出平行化天際線查詢演算法PSQ，支援漸進性(progressively)輸出，且適用於共享記憶體(shared memory)架構。PSQ演算法以維度(dimension)大小排序每筆資料(point)，作個簡單分類，再以分隔點(terminating point)機制減少資料間的比較，可大幅提升運算效率。我們以四種不同的軟硬體組合實作PSQ演算法。從實驗結果得知，PSQ演算法在這四種模擬環境中，維度個數多或資料量大時，都能有效利用多核心達到顯著的平行化效果。

The skyline query is an efficient data analysis tool for multi-criteria decision making that has received significant attention in the database community. With the advent of multi-core processors, we present a new parallel skyline query algorithm PSQ that can be applied to shared memory architectures, to progressively return skyline points as they are identified. The PSQ algorithm sorts the given dataset in descending order for coordinate component separately, and then finds out a terminating point for eliminating redundant computations of skyline query. Experimental results show that the PSQ algorithm successfully uses multiple cores to improve performance when dimensionality of the dataset grows or number of data points is large.

摘要
ABSTRACT
目錄
圖索引
表索引
第一章 緒論
1.1 研究動機
1.2 研究背景
1.2.1 多核心處理器
1.2.2 平行運算
1.2.3 平行化語言
1.2.3.1 OpenMp
1.2.3.2 Java
1.2.3.3 MPI
1.3論文架構 9
第二章 文獻探討 10
2.1 天際線查詢
2.2 分散式天際線查詢
2.3 多核心架構天際線查詢
第三章 研究方法
3.1 問題描述
3.1.1 問題定義
3.1.2 問題範例
3.1.3 問題策略
3.2 研究方法
3.2.1 PSkyline演算法
3.2.2 PSQ演算法
第四章 實驗結果
4.1 實驗設定
4.2 模擬環境
4.3 效能分析
4.3.1 ICMJ模擬環境效能分析
4.3.2 ICMO模擬環境效能分析
4.3.3 ACWJ模擬環境效能分析
4.3.4 ACWO模擬環境效能分析
4.4 運算時間
4.4.1 ICMJ模擬環境運算時間
4.4.2 ICMO模擬環境運算時間
4.4.3 ACWJ模擬環境運算時間
4.4.4 ACWO模擬環境運算時間
第五章 結論
參考文獻

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