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Tackling the Six Fundamental Challenges of Big Data in Research Projects by Utilizing a Scalable and Modular Architecture

机译:通过利用可扩展和模块化建筑来解决研究项目中大数据的六个基本挑战

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Over the last decades the necessity for processing and storing huge amounts of data has increased enormously, especially in the fundamental research area. Beside the management of large volumes of data, research projects are facing additional fundamental challenges in terms of data velocity, data variety and data veracity to create meaningful data value. In order to cope with these challenges solutions exist. However, they often show shortcomings in adaptability, usability or have high licence fees. Thus, this paper proposes a scalable and modular architecture based on open source technologies using micro-services which are deployed using Docker. The proposed architecture has been adopted, deployed and tested within a current research project. In addition, the deployment and handling is compared with another technology. The results show an overcoming of the fundamental challenges of processing huge amounts of data and the handling of Big Data in research projects.
机译:在过去的几十年中,加工和储存大量数据的必要性大规模增加,特别是在基本研究区域。 除了管理大量数据的管理外,研究项目在数据速度,数据品种和数据准确性方面面临额外的基本挑战,以创建有意义的数据价值。 为了应对这些挑战,存在解决方案。 但是,它们通常会显示适应性,可用性或具有高许可证费用的缺点。 因此,本文提出了一种基于使用Docker部署的微服务的开源技术的可扩展和模块化架构。 在当前的研究项目中,已采用,部署和测试了拟议的架构。 此外,将部署和处理与另一技术进行比较。 结果表明克服了加工大量数据和研究项目中大数据的基本挑战。

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