Conversational interfaces are increasingly used for data analysis, enabling data workers to express complex analytical intents in natural language. Yet, these interactions unfold as long, linear transcripts that are misaligned with the iterative, nonlinear nature of real-world analyses. Revisiting and summarizing conversations for different contexts is therefore challenging. This paper investigates how data workers navigate, make sense of, and communicate prior analytical conversations. To study behaviors beyond those supported by standard interfaces (i.e., scrolling and keyword search), we develop a design probe that supplements analytical conversations with structured elements and affordances (e.g., filtering, multi-level navigation and detail-on-demand). In a user study (n = 10), participants used the probe to navigate and communicate past analyses, fulfilling information needs (recall, reorient, prioritize) through navigation strategies (visual recall, sequential and abstractive) and summarization practices (adding process details and context). Based on these findings, we discuss design implications to support re-visitation and communication of analytical conversations.

翻译：对话式界面在数据分析中的应用日益广泛，使得数据工作者能够以自然语言表达复杂的分析意图。然而，这些交互以冗长、线性的对话记录形式展开，与现实世界中迭代式、非线性的分析本质不相匹配。因此，在不同情境下回顾和总结对话内容具有挑战性。本文研究了数据工作者如何导航、理解并交流先前的分析对话。为了探究标准界面功能（如滚动和关键词搜索）之外的行为模式，我们开发了一个设计探针，通过引入结构化元素和交互支持（例如过滤、多级导航和按需细节展示）来增强分析对话的可用性。在一项用户研究（n = 10）中，参与者使用该探针导航并交流过往分析，通过导航策略（视觉回溯、顺序浏览与抽象归纳）和总结实践（补充过程细节与上下文），满足了信息需求（回溯、重定向、优先级排序）。基于这些发现，我们讨论了支持分析对话重访与交流的设计启示。