English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 109952/140887 (78%)
Visitors : 46321602      Online Users : 1005
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/122029


    Title: An Infrastructure and Application of Computational Archival Science to Enrich and Integrate Big Digital Archival Data: Using Taiwan Indigenous Peoples Open Research Data (TIPD) as Example
    Authors: 林季平
    Lin, Ji-Ping
    Contributors: 社會系
    Keywords: Big Data;data mining;demography;information retrieval systems;public domain software;records management;time series
    Date: 2017-12
    Issue Date: 2019-01-21 13:56:00 (UTC+8)
    Abstract: This paper highlights research on constructing a big archival data called Taiwan Indigenous Peoples Open Research Data (TIPD, see https://osf.io/e4rvz/) based on contemporary census and household registration data sets in 2013-2017 (see http://TIPD.sinica.edu.tw). TIPD utilizes record linkage, geocoding, and high-performance in-memory computing technology to construct various dimensions of Taiwan Indigenous Peoples (TIPs) demographics and developments. Embedded in collecting, cleaning, cleansing, processing, exploring, and enriching individual digital records are archival computational science and data science. TIPD consists of three categories of archival open data: (1) categorical data, (2) household structure and characteristics data, and (3) population dynamics data, including cross-sectional time-series categorical data, longitudinally linked population dynamics data, life tables, household statistics, micro genealogy data, marriage practice and ethnic identity data, internal migration data, geocoded data, etc. TIPD big archival data not only help unveil contemporary TIPs demographics and various developments, but also help overcome research barriers and unleash creativity for TIPs studies.
    Relation: 2017 IEEE International Conference on Big Data (Big Data) , The IEEE Computer Society Press,
    Data Type: book/chapter
    DOI 連結: https://doi.org/10.1109/BigData.2017.8258181
    DOI: 10.1109/BigData.2017.8258181
    Appears in Collections:[社會學系] 專書/專書篇章

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML2666View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback