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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68823

    Title: 基於Hadoop平台之分散式權重式字尾樹暨天文時序性資料分析系統;Distributed Astronomy Sequential Pattern Analysis System Using Hadoop Platform with Weighted Suffix Tree
    Authors: 蔡昀翰;Tsai,Yun-Hang
    Contributors: 資訊工程學系
    Keywords: 泛星計畫;分散式系統;資料探勘;權重字尾樹;Pan-Starrs;Distributed System;Data Mining;Weighted Suffix Tree
    Date: 2015-07-28
    Issue Date: 2015-09-23 14:42:52 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著科技的發展,泛星計畫(Panoramic Survey Telescope And Rapid Response System,Pan-STARRS)中所觀測到的資料量也隨之增長,而儲存設備成本降低,也讓天文學家們得以將大量且詳細的觀測資料儲存起來。
    ;Because of the ongoing construction of observatories from Pan-Starrs projects with technological advancements, the size of observation data has exploded. And the storage device cost reduction. Astronomical researchers were able to make a large and detailed observation data stored.
    The various elements of collected astronomical data have time sequential features. And the traditional method is difficult to handle such data. So we use the suffix tree as a prototype of system structure to provide astronomical researchers a fast and efficient data query system. And we can provide approximate patterns to astronomical researchers after finish the analysis.
    Because the interaction of the amazing memory consumed of suffix tree data structure and the very large number of astronomical data lead to a single machine overload, we use the open source OpenStack system to construct Hadoop platform cloud system to complete a distributed environment. So that we can process astronomical data distributed, and enhance the effectiveness of the system.
    To Process large amounts of astronomical data through distributed systems can reduce the cost of manually data processing and the efficiency has been significantly improved. We provided a valid solution when astronomical researchers face a lot of observation data in the future. We hope to use this system architecture to analyze all the time sequential data in the future.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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