从执行跟踪中检测业务流程中的突然和逐渐漂移
Detecting sudden and gradual drifts in business processes from execution traces
摘要：
业务流程很容易发生意外的变化，因为流程工作人员可能会突然或逐渐开始以不同的方式执行流程，以适应工作负载、季节或其他外部因素的变化。业务流程变更的早期检测使管理人员能够识别可能影响流程绩效的变更并采取相应行动。业务流程漂移检测是指通过分析从支持流程执行的系统中提取的事件日志来检测业务流程中的更改的一系列方法。现有的业务流程漂移检测方法是基于一个潜在的大特征空间的探索性分析，在某些情况下，他们需要用户手动识别特定的功能，表征漂移。根据探索的特征空间，这些方法会错过各种类型的更改。此外，它们要么被设计成检测突然漂移，要么被设计成检测逐渐漂移，但不能同时检测两者。本文提出了一种自动化和统计接地的方法来检测突然和渐进的业务流程漂移下一个统一的框架。实证评估表明，该方法检测典型的变化模式，具有显着更高的精度和更低的检测延迟比现有的方法，同时准确区分突然和逐渐漂移。
英文摘要：
Business processes are prone to unexpected changes, as process workers may suddenly or gradually start executing a process differently in order to adjust to changes in workload, season, or other external factors. Early detection of business process changes enables managers to identify and act upon changes that may otherwise affect process performance. Business process drift detection refers to a family of methods to detect changes in a business process by analyzing event logs extracted from the systems that support the execution of the process. Existing methods for business process drift detection are based on an explorative analysis of a potentially large feature space and in some cases they require users to manually identify specific features that characterize the drift. Depending on the explored feature space, these methods miss various types of changes. Moreover, they are either designed to detect sudden drifts or gradual drifts but not both. This paper proposes an automated and statistically grounded method for detecting sudden and gradual business process drifts under a unified framework. An empirical evaluation shows that the method detects typical change patterns with significantly higher accuracy and lower detection delay than existing methods, while accurately distinguishing between sudden and gradual drifts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
