迈向推特观测站:收集、存储和分析推特的多范式框架

Ian Basaille, Sergey Kirgizov, É. Leclercq, M. Savonnet, N. Cullot
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引用次数: 5

摘要

在本文中,我们展示了一个多范式框架如何满足推文分析的要求,并减少使用计算资源和存储系统来支持大规模数据分析的研究人员的等待时间。我们的方法的独创性在于将对数据收集、数据存储、数据分析和数据可视化的关注结合到一个框架中,该框架支持多学科科学研究中的归纳推理。我们的主要贡献是一个多语言存储系统,它具有一个通用的数据模型来支持逻辑数据独立性,以及一组工具,可以为混合不同类型的算法提供合适的解决方案,以最大限度地提取知识。我们描述了我们的框架的软件架构,通用模型,并展示了它是如何在主要项目中使用的,以及验证了哪些特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Towards a Twitter observatory: A multi-paradigm framework for collecting, storing and analysing tweets
In this article we show how a multi-paradigm framework can fulfil the requirements of tweets analysis and reduce the waiting time for researchers that use computational resources and storage systems to support large-scale data analysis. The originality of our approach is to combine concerns about data harvesting, data storage, data analysis and data visualisation into a framework that supports inductive reasoning in multidisciplinary scientific research. Our main contribution is a polyglot storage system with a generic data model to support logical data independence and a set of tools that can provide a suitable solution for mixing different types of algorithms in order to maximise the extraction of knowledge. We describe the software architecture of our framework, the generic model and we show how it has been used in major projects and what characteristics have been validated.
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