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The Six Pi11ars for Building Big Data Analytics Ecosystems

机译:建立大数据分析生态系统的六个方面

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摘要

With almost everything now online, organizations look at the Big Data collected to gain insights for improving their services. In the analytics process, derivation of such insights requires experimenting-with and integrating different analytics techniques, while handling the Big Data high arrival velocity and large volumes. Existing solutions cover bits-and-pieces of the analytics process, leaving it to organizations to assemble their own ecosystem or buy an off-the-shelf ecosystem that can have unnecessary components to them. We build on this point by dividing the Big Data Analytics problem into six main pillars. We characterize and show examples of solutions designed for each of these pillars. We then integrate these six pillars into a taxonomy to provide an overview of the possible state-of-the-art analytics ecosystems. In the process, we highlight a number of ecosystems to meet organizations different needs. Finally, we identify possible areas of research for building future Big Data Analytics Ecosystems.
机译:几乎所有内容都在线上,组织可以查看收集的大数据,以获取改进服务的见解。在分析过程中,要获得此类洞察力,需要进行试验并整合不同的分析技术,同时处理大数据的高到达速度和大数据量。现有的解决方案涵盖了分析过程的各个环节,让组织来组装自己的生态系统或购买可能包含不必要组件的现成生态系统。在这一点上,我们将大数据分析问题划分为六个主要支柱。我们描述并展示了针对每个支柱设计的解决方案的示例。然后,我们将这六个支柱整合到一个分类法中,以概述可能的最新分析生态系统。在此过程中,我们重点介绍了许多生态系统,以满足组织的不同需求。最后,我们确定了构建未来大数据分析生态系统的可能研究领域。

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