标题： 从混沌到自动化：使非结构化数据用于机器人流程自动化的可能性 显示英文标题

标题： From Chaos to Automation: Enabling the Use of Unstructured Data for Robotic Process Automation

摘要： 组织内部非结构化数据量的增加给数据分析和流程自动化带来了重大挑战。 非结构化数据缺乏预定义的格式，包括电子邮件、报告和扫描件等多种形式。 据估计，它约占企业数据的80%。 尽管它可以提供有价值的信息，但与结构化数据相比，从非结构化数据中提取有意义的信息更加复杂。 机器人流程自动化（RPA）因其能够自动化重复任务、提高效率和减少错误而受到欢迎。 然而，RPA传统上依赖于结构化数据，这限制了其在涉及非结构化文档的流程中的应用。 本研究通过开发UNstructured Document REtrieval SyStem（UNDRESS），解决了这一局限性，该系统使用模糊正则表达式、自然语言处理技术以及大语言模型，使RPA平台能够有效地从非结构化文档中检索信息。 研究涉及原型系统的设 计和开发，并随后基于文本提取和信息检索性能进行评估。 结果表明，UNDRESS在增强RPA处理非结构化数据的能力方面是有效的，为该领域提供了重要进展。 研究结果表明，该系统可能有助于在传统上受非结构化数据阻碍的流程中更广泛地采用RPA，从而提高整体业务流程效率。 显示英文摘要

摘要： The growing volume of unstructured data within organizations poses significant challenges for data analysis and process automation. Unstructured data, which lacks a predefined format, encompasses various forms such as emails, reports, and scans. It is estimated to constitute approximately 80% of enterprise data. Despite the valuable insights it can offer, extracting meaningful information from unstructured data is more complex compared to structured data. Robotic Process Automation (RPA) has gained popularity for automating repetitive tasks, improving efficiency, and reducing errors. However, RPA is traditionally reliant on structured data, limiting its application to processes involving unstructured documents. This study addresses this limitation by developing the UNstructured Document REtrieval SyStem (UNDRESS), a system that uses fuzzy regular expressions, techniques for natural language processing, and large language models to enable RPA platforms to effectively retrieve information from unstructured documents. The research involved the design and development of a prototype system, and its subsequent evaluation based on text extraction and information retrieval performance. The results demonstrate the effectiveness of UNDRESS in enhancing RPA capabilities for unstructured data, providing a significant advancement in the field. The findings suggest that this system could facilitate broader RPA adoption across processes traditionally hindered by unstructured data, thereby improving overall business process efficiency.