Tessera: Discretizing Data Analysis Workflows on a Task Level
Tessera: Discretizing Data Analysis Workflows on a Task Level
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DOI：
10.1145/3411764.3445728
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发表时间：
2021-05
期刊：
影响因子：
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通讯作者：
Jing Nathan Yan;Ziwei Gu;Jeffrey M. Rzeszotarski
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文献类型：
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作者：
Jing Nathan Yan;Ziwei Gu;Jeffrey M. Rzeszotarski
研究人员已经研究了许多用于捕获和分析数据分析师事件日志的策略，以便设计更好的工具，识别故障点并指导用户。然而，这仍然具有挑战性，因为个人和会话级别的行为差异会导致复杂性的爆炸式增长，并且很难保证日志观察结果映射到用户认知。在本文中，我们介绍了一种技术，用于分割连续的分析师事件日志，它结合了数据，交互和用户功能，以创建离散块的目标导向的活动。使用相互依赖性的度量和分析状态之间的比较，这些块识别交互日志中的模式以及用户正在检查的当前视图。通过对公开数据和实验室研究中各种分析任务的数据进行分析，我们验证了我们的细分方法与用户不断变化的目标和任务保持一致。最后，我们确定了我们的方法的几个下游应用程序。
Researchers have investigated a number of strategies for capturing and analyzing data analyst event logs in order to design better tools, identify failure points, and guide users. However, this remains challenging because individual- and session-level behavioral differences lead to an explosion of complexity and there are few guarantees that log observations map to user cognition. In this paper we introduce a technique for segmenting sequential analyst event logs which combines data, interaction, and user features in order to create discrete blocks of goal-directed activity. Using measures of inter-dependency and comparisons between analysis states, these blocks identify patterns in interaction logs coupled with the current view that users are examining. Through an analysis of publicly available data and data from a lab study across a variety of analysis tasks, we validate that our segmentation approach aligns with users’ changing goals and tasks. Finally, we identify several downstream applications for our approach.