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：数据探索在数据分析中至关重要，它能帮助用户更有效地理解和解释数据。然而，开展高效的数据探索需要对数据集的深入理解以及数据分析技术的专业知识。缺乏其中任一条件都会造成障碍，使数据分析师感到过程耗时且困难重重。为解决这一问题，我们提出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。InsightPilot自动选择合适的分析意图，例如理解、总结和解释。随后，通过发出相应的意图查询（IQuery）将这些分析意图具体化，从而生成一条有意义且连贯的探索序列。简而言之，IQuery是对数据分析操作的抽象与自动化，它模拟数据分析师的工作方式，简化用户的探索过程。通过利用大语言模型经由IQuery与先进洞察引擎迭代协作，InsightPilot能够有效分析真实世界数据集，使用户能够通过自然语言查询获取宝贵见解。我们通过案例研究展示了InsightPilot的有效性，说明它如何帮助用户从数据中获得有价值的洞察。