Process mining, as a high-level field in data mining, plays a crucial role in enhancing operational efficiency and decision-making across organizations. In this survey paper, we delve into the growing significance and ongoing trends in the field of process mining, advocating a specific viewpoint on its contents, application, and development in modern businesses and process management, particularly in cross-organizational settings. We first summarize the framework of process mining, common industrial applications, and the latest advances combined with artificial intelligence, such as workflow optimization, compliance checking, and performance analysis. Then, we propose a holistic framework for intelligent process analysis and outline initial methodologies in cross-organizational settings, highlighting both challenges and opportunities. This particular perspective aims to revolutionize process mining by leveraging artificial intelligence to offer sophisticated solutions for complex, multi-organizational data analysis. By integrating advanced machine learning techniques, we can enhance predictive capabilities, streamline processes, and facilitate real-time decision-making. Furthermore, we pinpoint avenues for future investigations within the research community, encouraging the exploration of innovative algorithms, data integration strategies, and privacy-preserving methods to fully harness the potential of process mining in diverse, interconnected business environments.

翻译：流程挖掘作为数据挖掘领域的高级分支，在提升跨组织运营效率与决策支持方面发挥着关键作用。本综述论文深入探讨了流程挖掘领域日益增长的重要性与发展趋势，针对现代企业及流程管理（特别是在跨组织场景中）的内容体系、应用实践与发展方向提出了特定观点。我们首先概述了流程挖掘的基本框架、常见工业应用场景，以及结合人工智能技术的最新进展，如工作流优化、合规性检查与性能分析。随后，我们提出了智能流程分析的整体框架，并勾勒出跨组织场景下的初步方法论，同时着重指出该领域面临的挑战与机遇。这一独特视角旨在通过运用人工智能技术为复杂的多组织数据分析提供精细化解决方案，从而推动流程挖掘领域的变革。通过整合先进的机器学习技术，我们可以增强预测能力、优化流程并促进实时决策。此外，我们明确了未来研究方向的潜在路径，鼓励学术界探索创新算法、数据集成策略与隐私保护方法，以充分释放流程挖掘在多样化、互联互通的商业环境中的潜力。