Please use this identifier to cite or link to this item: https://ah.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
Appears in Collections:[社會學系] 專書/專書篇章

Files in This Item:

File Description SizeFormat
index.html0KbHTML343View/Open


All items in 學術集成 are protected by copyright, with all rights reserved.


社群 sharing