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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72051

    Title: 以MapReduce進行交叉驗證整合大量天文資料;Incorporating Astronomical Catalog by using Cross-Matching Algorithm with MapReduce
    Authors: 謝佳昕;Shie,Jia-Shin
    Contributors: 資訊工程學系
    Keywords: 大量資料;雲端運算;分散式系統;交叉驗證;Big Data;Cloud computing;Distributed system;Cross-Matching
    Date: 2016-07-20
    Issue Date: 2016-10-13 14:23:34 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著科技的進步,在天文觀測時所使用的望遠鏡的功能也越來越強大,所觀測到的資訊更多、資料量也更大。增加了天文研究人員在進行研究時的困難,因此,本論文提出以交叉驗證(Cross-Matching)的方式,對大量的天文資料進行整合,以利於研究人員能快速的找出所需的資料。
    ;Cross-Matching is a common way for find out the useful information from different star catalogs. Today hardware is more powerful than before. The data obtained through astronomical telescopes are becoming much larger. Therefore, single machine is not able to afford handling the astronomical data. In this paper, we use OpenStack to build a cloud computing environment, Hadoop as a distributed system, HDFS and HBase as distributed storages. Implement Cross-matching with MapReduce framework. In addition, Hbase supports random access so we make an incremental mechanism. User can update new astronomical data as they want. In the experiment, Transient is my test data to compare the operation time of using single machine with distributed system and using the same number of nodes on the physical machine with virtual machine. The result shows that using virtual machine is faster than using physical machine. Furthermore, we create 12 physical nodes on cloud environment to observe the operation time of different number of node. Theoretically, when we use more nodes to run the program the speed is much faster. The fact that the speeds of 10 nodes and 12 nodes are very similar.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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