标题： UniDataBench：跨结构化和非结构化数据评估数据分析代理 显示英文标题

标题： UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data

摘要： 在现实的商业世界中，数据存储在各种来源中，包括结构化的关系型数据库、非结构化的数据库（例如，NoSQL数据库），甚至是CSV/Excel文件。 在这些多样化数据源之间提取合理的洞察力对于商业成功至关重要。 然而，现有的基准测试在评估代理在这些多样化数据类型上的能力方面存在局限性。 为了解决这一差距，我们引入了UniDataBench，一个全面的基准测试，旨在评估数据分析代理处理多样化数据源的性能。 具体来说，UniDataBench起源于真实行业的分析报告，然后我们提出了一条管道来移除隐私和敏感信息。 它涵盖了广泛的数据集，包括关系型数据库、CSV文件到NoSQL数据，反映了现实世界的商业场景，并提供了统一的框架来评估代理如何有效地探索多种数据格式，提取有价值的洞察力，并生成有意义的总结和建议。 基于UniDataBench，我们提出了一种名为ReActInsight的新颖的基于LLM的代理，这是一个自主代理，通过自动发现跨源关联、分解目标并生成稳健的自我纠正代码来提取可操作的洞察力，从而对多样化数据源进行端到端分析。 我们的基准测试和代理共同提供了一个强大的框架，以推动数据分析代理在实际应用中的能力。 显示英文摘要

摘要： In the real business world, data is stored in a variety of sources, including structured relational databases, unstructured databases (e.g., NoSQL databases), or even CSV/excel files. The ability to extract reasonable insights across these diverse source is vital for business success. Existing benchmarks, however, are limited in assessing agents' capabilities across these diverse data types. To address this gap, we introduce UniDataBench, a comprehensive benchmark designed to evaluate the performance of data analytics agents in handling diverse data sources. Specifically, UniDataBench is originating from real-life industry analysis report and we then propose a pipeline to remove the privacy and sensitive information. It encompasses a wide array of datasets, including relational databases, CSV files to NoSQL data, reflecting real-world business scenarios, and provides unified framework to assess how effectively agents can explore multiple data formats, extract valuable insights, and generate meaningful summaries and recommendations. Based on UniDataBench, we propose a novel LLM-based agent named ReActInsight, an autonomous agent that performs end-to-end analysis over diverse data sources by automatically discovering cross-source linkages, decomposing goals, and generating robust, self-correcting code to extract actionable insights. Our benchmark and agent together provide a powerful framework for advancing the capabilities of data analytics agents in real-world applications.