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,它有助于用户更有效地理解和解释数据。然而,有效的数据探索需要对数据集有深入的了解,并具有数据分析技术的专业知识。如果缺乏这两者中的任何一项,都会导致数据分析师在探索过程中遇到困难和耗费时间。为了解决这个问题,我们引入了一个基于LLM(大型语言模型)的自动化数据探索系统,名为InsightPilot,旨在简化数据探索过程。InsightPilot自动选择适当的分析意图,例如理解、总结和解释。随后,这些分析意图通过发出相应的意图查询(IQueries)来具体化,以创建有意义和连贯的探索序列。简而言之,IQuery是数据分析操作的抽象和自动化,模仿数据分析员的方法,为用户简化了探索过程。通过使用LLM通过IQueries与最先进的洞见引擎进行迭代协作,InsightPilot在分析真实世界的数据集方面非常有效,使用户通过自然语言查询获得有价值的洞见。我们在一个案例研究中展示了InsightPilot的有效性,展示了它如何帮助用户从其数据集中获得有价值的洞见。