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Developer toolchains for large-scale analytics: Two case studies

机译:用于大规模分析的开发者工具链:两种案例研究

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While big data analytics continue to grow in popularity among companies and organizations, their large-scale analytic implementations are often completed by software developers with little or no formal training in machine learning or data analysis. These developers are skilled at writing code but they do not have the understanding of the data analytics process to be efficient or necessarily accurate at it. These developers use processes and tools that are often ad hoc and incomplete as they learn by doing. We followed a development team through two analytics development cycles and analyzed their interactions with their data and tools. In this paper, we first describe the tools the developers used and then present concrete opportunities for the big data community to create tools that empower these developers to build more accurate analytics more efficiently.
机译:虽然在公司和组织中,大数据分析在流行过程中持续增长,但它们的大规模分析实现通常由软件开发人员完成,在机器学习或数据分析中很少或没有正式培训。这些开发人员擅长编写代码,但他们没有了解数据分析过程以高效或必然准确。这些开发人员使用往往的流程和工具以及他们通过DIME学习时的临时和不完整。我们通过两个分析开发周期进行了开发团队,并分析了与他们的数据和工具的互动。在本文中,我们首先描述了开发人员使用的工具,然后向大数据社区呈现具体机会,以创建授权这些开发人员更有效地构建更准确的分析的工具。

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