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Collaborative visual analysis with RCloud

机译：与RCLOD的协作视觉分析

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Consider the emerging role of data science teams embedded in larger organizations. Individual analysts work on loosely related problems, and must share their findings with each other and the organization at large, moving results from exploratory data analyses (EDA) into automated visualizations, diagnostics and reports deployed for wider consumption. There are two problems with the current practice. First, there are gaps in this workflow: EDA is performed with one set of tools, and automated reports and deployments with another. Second, these environments often assume a single-developer perspective, while data scientist teams could get much benefit from easier sharing of scripts and data feeds, experiments, annotations, and automated recommendations, which are well beyond what traditional version control systems provide. We contribute and justify the following three requirements for systems built to support current data science teams and users: discoverability, technology transfer, and coexistence. In addition, we contribute the design and implementation of RCloud, a system that supports the requirements of collaborative data analysis, visualization and web deployment. About 100 people used RCloud for two years. We report on interviews with some of these users, and discuss design decisions, tradeoffs and limitations in comparison to other approaches.
机译：考虑较大组织中嵌入的数据科学团队的新兴作用。个人分析师在松散相关的问题上工作，并且必须将其调查结果彼此分享到大量的，从探索性数据分析（EDA）进入自动化的可视化，诊断和报告以进行更广泛的消费。目前的做法有两个问题。首先，在此工作流程中存在空白：使用一组工具执行EDA，并使用另一组工具和自动报告和部署。其次，这些环境通常假设单一开发人员的角度，而数据科学家团队可以从更容易分享脚本和数据馈送，实验，注释和自动建议的情况下获得许多好处，这远远超出了传统版本控制系统提供的范围。我们为支持当前数据科学团队和用户提供的系统提供了以下三个要求：可发现，技术转让和共存。此外，我们还提供了一个支持协作数据分析，可视化和Web部署要求的系统的设计和实现。大约100人使用rcloud两年。我们报告了一些关于这些用户的访谈，与其他方法相比，讨论设计决策，权衡和限制。

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