论文标题
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
论文作者
论文摘要
业务流程容易发生意外变化，因为过程工作者可能会突然或逐渐开始以不同的方式执行过程，以适应工作量，季节或其他外部因素的变化。早期检测业务流程变化使管理人员能够识别并采取可能影响过程绩效的变化。业务流程漂移检测是指通过分析从支持该流程执行的系统中提取的事件日志来检测业务流程变化的一种方法。现有的业务流程漂移检测方法基于对潜在的大特征空间的探索性分析，在某些情况下，他们要求用户手动确定表征漂移的特定功能。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，它们的目的是检测突然漂移或逐渐漂移，但并非两者兼而有之。本文提出了一种自动化和统计基础的方法，用于检测统一框架下突然和逐步的业务流程漂移。经验评估表明，该方法检测到典型的变化模式，其精度明显更高，检测延迟明显高于现有方法，同时准确区分了突然和逐渐漂移。
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.
