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| Title: | 基於MapReduce框架進行有效的天際線查詢處理
Efficient Skyline Query Processing with MapReduce |
| Authors: | 詹智渝
Chan, Chih Yu |
| Contributors: | 陳良弼
Chen, Arbee L.P.
詹智渝
Chan, Chih Yu |
| Keywords: | 天際線查詢
巨量資料
分散式運算 |
| Date: | 2013 |
| Issue Date: | 2013-11-01 11:44:16 (UTC+8) |
| Abstract: | 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. |
| Reference: | [1] J. Dean, and S. Ghemawat, “MapReduce: Simplified Data Processing on Large Cluster,” in Proceedings of the Operating Systems Design and Implementation, 2004.
[2] S. Borzsonyi, D. Kossmann, and K. Stocker, “The Skyline Operator,” in Proceedings of the International Conference on Data Engineering, 2001.
[3] B. L. Zhang, S. G. Zhou, and J. H. Guan, “Adapting Skyline computation to the MapReduce Framework: Algorithms and Experiments,” in Proceeding of the Database Systems for Advanced Applications workshop, 2011.
[4] L. L. DING, J. C. XIN, G. R. WANG, and S. HUANG, “Efficient Skyline Query Processing of Massive Data Based on Map-Reduce,” in Chinese Journal of Computers, 2012.
[5] J. Chomicki, P. Godfery, J. Gryz, and D. Liang, “Skyline with presorting,” in Proceedings of the International Conference on Data Engineering, 2003.
[6] J. Chomicki, P. Godfrey, J. Gryz, and D. Liang, “Skyline with presorting: Theory and optimizations,” in Journal of the Intelligent Information Systems, 2005.
[7] P. Godfrey, R. Shipley, and J. Gryz, “Maximal vector computation in large data Sets,” in Proceedings of the Very Large Databases, 2005.
[8] I. Bartolini, P. Ciaccia, and M. Patella, “SaLSa: Computing the Skyline without Scanning the Whole Sky,” in Proceeding of the Conference on Information and Knowledge Management, 2006.
[9] 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.
[10] 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.
[11] D. Papadias, Y. Tao, G. Fu, and B. Seeger, “Progressive Skyline computation in database systems,” in Proceedings of the Transactions on Database Systems, 2005.
[12] S. M. Zhang, N. Mamoulis, and D. W. Cheung, “Scalable Skyline Computation Using Object-based Space Partitioning,” in Proceedings of the ACM International Conference on Management of Data, SIGMOD, 2009
[13] B. Cui, H. Lu, Q. Xu, L. Chen, Y. Dai, and Y. Zhou, “Parallel distributed processing of constrained Skyline queries by filtering,” in Proceedings of the International Conference on Data Engineering, 2008.
[14] J.B. Rocha-Junior, A. Vlachou, C. Doulkeridis, and K. Nørvåg, “Efficient execution plans for distributed Skyline query processing,” in Proceedings of the Extending Database Technology, 2011.
[15] A. Vlachou, C. Doulkeridis, and Y. Kotidis, “Angle-based space partitioning for efficient parallel Skyline computation,” in Proceedings of the ACM International Conference on Management of Data, SIGMOD, 2008.
[16] H. Köhler, J. Yang, and X. Zhou, “Efficient Parallel Skyline Processing using Hyperplane Projections,” in Proceedings of the ACM International Conference on Management of Data, SIGMOD, 2011. |
| Description: | 碩士
國立政治大學
資訊科學學系
100753037
102 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0100753037 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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