Digital archives as Big data

IF 1.4 3区社会学 Q3 DEMOGRAPHY

Mathematical Population Studies Pub Date : 2018-02-26 DOI:10.1080/08898480.2017.1418116

L. Martinez-Uribe

引用次数: 2

Abstract

ABSTRACT Digital archives contribute to Big data. Combining social network analysis, coincidence analysis, data reduction, and visual analytics leads to better characterize topics over time, publishers’ main themes and best authors of all times, according to the British newspaper The Guardian and from the 3 million records of the British National Bibliography.

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作为大数据的数字档案

摘要数字档案为大数据做出了贡献。根据英国《卫报》和英国国家书目的300万条记录，将社交网络分析、巧合分析、数据缩减和视觉分析相结合，可以更好地描述一段时间以来的主题、出版商的主要主题和有史以来的最佳作者。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Mathematical Population Studies 数学-数学跨学科应用

CiteScore

3.20

自引率

11.10%

发文量

审稿时长

>12 weeks

期刊介绍： Mathematical Population Studies publishes carefully selected research papers in the mathematical and statistical study of populations. The journal is strongly interdisciplinary and invites contributions by mathematicians, demographers, (bio)statisticians, sociologists, economists, biologists, epidemiologists, actuaries, geographers, and others who are interested in the mathematical formulation of population-related questions. The scope covers both theoretical and empirical work. Manuscripts should be sent to Manuscript central for review. The editor-in-chief has final say on the suitability for publication.