LogRank翻译：支持流程事件日志采样的新方法

翻译论文LogRank: 一种支持业务流程事件日志采样的新方法，这篇论文研究了当代信息系统收集和存储的大量业务流程事件日志。过去几十年中，已经提出了许多流程发现方法，用于从这些事件日志中提取描述性流程模型。为了提高流程发现效率，提出了事件日志采样技术。样本日志是原始日志的精心选取的子集。 LogRank是一种新颖的支持业务流程事件日志采样的方法。 Abstract. Massive amounts of business process event logs are collected and stored by modern information systems. Numerous process discovery approaches have been proposed to extract descriptive process models from such event logs in the past decades. To improve process discovery efficiency, event log sampling techniques are proposed. A sample log is a delicately selected subset of the original log, designed to capture the essential characteristics of the process while reducing the volume of data to be analyzed. LogRank is a novel approach to support business process event log sampling, which utilizes statistical techniques to determine the optimal sample size and composition for achieving accurate process discovery results. Through experimental evaluation, LogRank has been shown to outperform existing sampling methods in terms of both accuracy and efficiency. This paper provides a detailed description of the LogRank methodology, along with empirical evidence of its effectiveness in real-world applications. Overall, LogRank represents a significant advancement in the field of process mining and offers a practical solution for improving the analysis of business process event logs. 在当代信息系统中，大量的业务流程事件日志被收集并存储。在过去几十年中，已经提出了许多流程发现方法，用于从这些事件日志中提取描述性流程模型。为了提高流程发现效率，提出了事件日志采样技术。样本日志是原始日志的精心选取的子集，旨在捕捉流程的基本特征，同时减少需要分析的数据量。 LogRank是一种新颖的支持业务流程事件日志采样的方法，利用统计技术确定实现准确流程发现结果的最佳样本大小和组成。通过实验评估，LogRank已被证明在准确性和效率方面优于现有的采样方法。本文详细描述了LogRank方法论，并提供了其在实际应用中有效性的经验证据。总的来说，LogRank代表了过程挖掘领域的重大进步，并为改进业务流程事件日志分析提供了实用解决方案。