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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/61490


    Title: 以MapReduce做有效率的天際線查詢
    Efficient Skyline Computation with MapReduce
    Authors: 陳家慶
    Chen, Chia Ching
    Contributors: 陳良弼
    Chen, Arbee L.P.
    陳家慶
    Chen, Chia Ching
    Keywords: 巨量資料
    天際線
    Big Data
    Skyline
    MapReduce
    Date: 2013
    Issue Date: 2013-11-01 11:43:53 (UTC+8)
    Abstract: 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.
    Reference: [1] J. Dean and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Clusters,” in Proceedings of the Operating Systems Design and Implementation, 2004.
    [2] S. BÄorzsÄonyi, D. Kossmann, and K. Stocker, “The Skyline Operator,” in Proceedings of the International Conference on Data Engineering, 2001.
    [3] D. Kossmann, F. Ramsak, and S. Rost, “Shooting Stars in the Sky: An Online Algorithm for Skyline Queries,” in Proceedings of the Very Large Databases, 2002.
    [4] D. Papadias, Y. Tao, G. Fu, and B. Seeger, “An Optimal and Progressive Algorithm for Skyline Queries,” in Proceedings of ACM International Conference on Management of Data, 2003.
    [5] J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, “Skyline with Presorting: Theory and Optimizations,” in Journal of the Intelligent Information Systems, 2005.
    [6] J. Chomicki, P. Godfery, and J. Gryz, and D. Liang, “Skyline with Presorting,” in Proceedings of the International Conference on Data Engineering, 2003
    [7] P. Godfrey, R. Shipley, and J. Gryz, “Maximal Vector Computation,” in Proceedings of the Very Large Databases, 2005.
    [8] S. Zhang, N. Mamoulis, and D. W. Cheung, “Scalable Skyline Computation Using Object-Based Space Partitioning.” in Proceedings of ACM International Conference on Management of Data, 2009.
    [9] J. Lee and S. Hwang, “BSkyTree: Scalable Skyline Computation Using a Balanced Pivot Selection,” in Proceedings of the Extending Database Technology, 2010.
    [10] A. Cosgaya-Lozano, A. Rau-Chaplin, and N. Zeh, “Parallel Computation of Skyline Queries,” in Proceedings of the International Symposium on High Performance Computing Systems and Applications, 2007.
    [11] P. Wu, C. Zhang, Y. Feng, B. Y. Zhao, D. Agrawal, and A. E. Abbadi, “Parallelizing Skyline Queries for Scalable Distribution,” in Proceedings of the Extending Database Technology, 2006.
    [12] A. Vlachou, C. Doulkeridis, and Y. Kotidis, “Angle-Based Space Partitioning for Efficient Parallel Skyline Computation,” in Proceedings of ACM International Conference on Management of Data, 2008.
    [13] H. Kohler, J. Yang, and X. Zhou, “Efficient Parallel Skyline Processing Using Hyper Plane Projections,” in Proceedings of ACM International Conference on Management of Data, 2011.
    [14] Boliang Zhang, Shuigeng Zhou, and Jihong Guan, “Adapting Skyline Computation to the MapReduce Framework: Algorithms and Experiments,” in Proceedings of the International Conference on Database Systems for Advanced Applications, 2011.
    [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.
    Description: 碩士
    國立政治大學
    資訊科學學系
    100753002
    102
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0100753002
    Data Type: thesis
    Appears in Collections:[Department of Computer Science ] Theses

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