现代大数据分析平台架构

L. Zubyk, Yaroslav Zubyk
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引用次数: 0

摘要

大数据是影响世界工业的现代工具之一。它在确定企业和组织制定战略和政策的方式方面也起着重要作用。然而,由于非结构化数据在获取、收集、处理和建模方面存在困难,且非结构化数据通常具有保密性等特点,因此基于大数据的预测的学术研究非常有限。我们在生态系统的背景下定义大数据,用于商业决策的未来预测。单个组织很难拥有从其发现中获得战略业务价值的所有必要功能。这就是为什么不同的组织会建立和运营自己的分析生态系统,或者利用现有的分析生态系统。一个由数据、应用程序、平台、人才、合作伙伴和第三方服务提供商组成的分析生态系统可以让组织更加灵活,适应不断变化的需求。参与分析生态系统的组织不仅可以检查、学习和影响他们自己的业务流程,还可以检查、学习和影响他们合作伙伴的业务流程。本文介绍了流行的基于大数据的预测平台的体系结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Architecture of modern platforms for big data analytics
Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.
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