GDELT数据的高性能挖掘系统

2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) Pub Date : 2020-05-01 DOI:10.1109/IPDPSW50202.2020.00182

Konstantin Pogorelov, Daniel Thilo Schroeder, Petra Filkuková, J. Langguth

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引用次数: 4

摘要

我们设计了一个系统，可以有效地在内存中分析来自GDELT数据库的新闻事件数据。该系统的专业化使我们能够避免现有替代方案的低效率，并充分利用现代并行高性能计算硬件。然后，我们展示了一系列实验，展示了系统在包含超过10亿个新闻条目的整个GDELT 2.0数据库中分析相关性的能力。研究结果揭示了当今世界在线新闻的大趋势。

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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A System for High Performance Mining on GDELT Data

We design a system for efficient in-memory analysis of data from the GDELT database of news events. The specialization of the system allows us to avoid the inefficiencies of existing alternatives, and make full use of modern parallel high-performance computing hardware. We then present a series of experiments showcasing the system’s ability to analyze correlations in the entire GDELT 2.0 database containing more than a billion news items. The results reveal large scale trends in the world of today’s online news.

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来源期刊

2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)

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