Exploring data is crucial in data analysis, as it helps users understand and interpret the data more effectively. However, performing effective data exploration requires in-depth knowledge of the dataset and expertise in data analysis techniques. Not being familiar with either can create obstacles that make the process time-consuming and overwhelming for data analysts. To address this issue, we introduce InsightPilot, an LLM (Large Language Model)-based, automated data exploration system designed to simplify the data exploration process. InsightPilot automatically selects appropriate analysis intents, such as understanding, summarizing, and explaining. Then, these analysis intents are concretized by issuing corresponding intentional queries (IQueries) to create a meaningful and coherent exploration sequence. In brief, an IQuery is an abstraction and automation of data analysis operations, which mimics the approach of data analysts and simplifies the exploration process for users. By employing an LLM to iteratively collaborate with a state-of-the-art insight engine via IQueries, InsightPilot is effective in analyzing real-world datasets, enabling users to gain valuable insights through natural language inquiries. We demonstrate the effectiveness of InsightPilot in a case study, showing how it can help users gain valuable insights from their datasets.

翻译：数据探索是数据分析中的关键环节，它能帮助用户更有效地理解和解读数据。然而，进行有效的数据探索需要深入掌握数据集知识并具备数据分析技术专长。缺乏任一条件都可能造成障碍，使数据分析师在探索过程中耗费大量时间且倍感挑战。为解决此问题，我们推出了InsightPilot——一种基于大语言模型（LLM）的自动化数据探索系统，旨在简化数据探索流程。该系统能自动选择合适的分析意图（如理解、总结、解释等），随后通过发出相应的意图查询（IQuery）将这些分析意图具体化，从而形成有意义且连贯的探索序列。简而言之，IQuery是数据分析操作的一种抽象与自动化形式，它模拟了数据分析师的工作方式，简化了用户的探索过程。通过借助LLM与先进洞察引擎进行基于IQuery的迭代协作，InsightPilot能够有效分析真实世界数据集，使用户通过自然语言查询即可获取宝贵洞察。我们通过案例研究展示了InsightPilot的有效性，说明了它如何帮助用户从数据集中获得有价值的见解。