AI 中文总结
Notrix是一个交互式可视分析工具，通过将单元格分类为ML阶段并按结构聚类，支持对数百个计算笔记本进行集合级分析，实验表明其显著提升回答准确率并降低认知负荷。
AI 中文摘要
计算笔记本使问题解决过程可见，但通常一次只能查看一个笔记本。与此同时，在Kaggle等数据科学平台中，一场竞赛可能积累数百个笔记本。有效的集合级分析需要刻画所有笔记本中反复出现的解决方案模式，并隔离特定笔记本以供仔细检查和深入学习。然而，标准笔记本为此提供了共同基础。它们的工作流是非线性的，单元格不声明意图，相同的代码可能服务于不同目的，导致数百个笔记本成为独立的文档。在本文中，我们提出Notrix，一种交互式可视分析工具，用于将数百个笔记本作为一个集合进行剖析。受一项形成性研究（N=11）的启发，Notrix将每个单元格分类为十三个机器学习（ML）阶段之一，将每个笔记本转化为阶段序列，并按结构而非代码对这些序列进行聚类。为了在范围从整个集合缩小到单个单元格时保持表示不变，Notrix设计了三个协调视图——工作流、结构矩阵和细节——这些视图出现在所有四个粒度级别上。在一项受试者内研究（N=17）中，使用两个各含超过400个笔记本的Kaggle集合，我们观察到参与者使用Notrix回答关于所有笔记本的问题时准确率更高（中位数88%对50%），同时每分钟打开的笔记本减少了80%。值得注意的是，十四名参与者中有四名在未打开任何笔记本的情况下回答了问题（交互日志，N=14）。参与者还报告使用Notrix时心理需求、时间需求和压力显著降低（经Holm-Bonferroni校正）。
英文摘要
Computational notebooks make problem-solving visible, but typically only one notebook at a time. Meanwhile, in data science platforms like Kaggle, one competition can accumulate hundreds of notebooks. Effective collection-level analysis requires characterizing recurring solution patterns across all notebooks, as well as isolating specific notebooks for closer examination and learning. However, standard notebooks provide no common basis for this. Their workflows are nonlinear, cells declare no intent, and identical code can serve different ends, leaving hundreds of notebooks as separate documents. In this paper, we present Notrix, an interactive visual analytics tool for profiling hundreds of notebooks as one collection. Inspired by a formative study (N = 11), Notrix classifies every cell into one of thirteen machine learning (ML) stages, turning each notebook into a stage sequence, and clusters those sequences by structure rather than by code. To keep the representation constant as the scope narrows from the whole collection to a single cell, Notrix features three coordinated views---Workflow, Structural Matrix, and Detail---that appear at all four levels of granularity. In a within-subject study (N = 17) using two Kaggle collections of over 400 notebooks each, we observed participants answered questions about all notebooks more accurately with Notrix (median 88% vs. 50%) while opening 80% fewer notebooks per minute. Notably, four of the fourteen answered it without opening a single notebook (interaction logs, N = 14). Participants also reported significantly lower mental demand, temporal demand, and stress with Notrix (Holm-Bonferroni adjusted).
