Practical data analysis scenarios involve more than just the interpretation of data through visual and algorithmic analysis. Many real-world analysis environments involve multiple types of experts and analysts working together to solve problems and make decisions, adding organizational and social requirements to the mix. We aim to provide new knowledge about the role of provenance for practical problems in a variety of analysis scenarios central to national security. We present the findings from interviews with data analysts from domains, such as intelligence analysis, cyber-security, and geospatial intelligence. In addition to covering multiple analysis domains, our study also considers practical workplace implications related to organizational roles and the level of analyst experience. The results demonstrate how different needs for provenance depend on different roles in the analysis effort (e.g., data analyst, task managers, data analyst trainers, and quality control analysts). By considering the core challenges reported along with an analysis of existing provenance-support techniques through existing research and systems, we contribute new insights about needs and opportunities for improvements to provenance-support methods.

中文翻译：

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实践中的分析来源：来源在现实世界可视化和数据分析环境中的作用

实际的数据分析场景不仅仅涉及通过视觉和算法分析来解释数据。许多现实世界的分析环境涉及多种类型的专家和分析师一起工作来解决问题和做出决策，将组织和社会要求添加到组合中。我们的目标是提供关于起源在对国家安全至关重要的各种分析场景中实际问题的作用的新知识。我们展示了对来自情报分析、网络安全和地理空间情报等领域的数据分析师的采访结果。除了涵盖多个分析领域外，我们的研究还考虑了与组织角色和分析师经验水平相关的实际工作场所影响。结果表明，对来源的不同需求如何取决于分析工作中的不同角色（例如，数据分析师、任务经理、数据分析师培训师和质量控制分析师）。通过考虑报告的核心挑战以及通过现有研究和系统对现有来源支持技术的分析，我们为改进来源支持方法的需求和机会提供了新的见解。