【异常注入工具】AIR-BAGLE：An Interactive Root cause-Based Anomaly Genera
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2023-04-18
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AIR-BAGEL: An Interactive Root cause-Based Anomaly Generator for Event Logs
AIR-BAGEL:用于事件日志的交互式基于根因的异常发生器
Abstract—We describe AIR-BAGEL, a tool to generate pseudo-real trace-level
anomalies in event logs. Anomalies to be injected are defined by their root cause, i.e.,
resource behaviour or system malfunctioning. For each root cause, several anomaly
types can be specified, e.g., deleting, replacing or moving events in a trace. Root causes
and anomalies have been modelled based on existing literature on event log cleaning
and data quality analysis. AIR-BAGEL addresses the issue of unavailability of labelled
real world event logs for developing and evaluating event log cleaning and
reconstruction techniques and it represents a step forward compared to current
approaches in the literature that simply inject different types of anomalies randomly in
event logs.
摘要——我们描述了 AIR-BAGEL，这是一种在事件日志中生成伪真实轨迹级异
常的工具。要注入的异常由其根本原因定义，即资源行为或系统故障。对于每个
根因，可以指定几种异常类型，例如，删除、替换或移动跟踪中的事件。已根据
有关事件日志清理和数据质量分析的现有文献对根因和异常进行建模。 AIR-
BAGEL 解决了在开发和评估事件日志清理和重建技术时标记的真实世界事件
日志不可用的问题，与文献中简单地在事件日志中随机注入不同类型异常的当前
方法相比，它代表了向前迈进了一步。
Index Terms—event log, data quality, anomaly, process mining, event log cleaning.
索引词——事件日志，数据质量，异常，流程挖掘，事件日志清理。
I. INTRODUCTION
一、引言
Event logs captured in the real world are prone to errors [1]. These can stem from a
variety of root causes, such as system malfunctioning or human mistakes, resulting in
different types of errors, such as abnormal activity labels, or missing, duplicated, and
swapped events [1]–[8]. Errors in event logs hamper the possibility of extracting useful
insights from event log analysis. For instance, they clearly influence the models
obtained through process discovery and the fitness computation in conformance
checking. Therefore, one should aim at removing these errors before running event log
analytics.
在现实世界中捕获的事件日志很容易出错 [1]。这些可能源于各种根本原因，例
如系统故障或人为错误，导致不同类型的错误，例如异常活动标签，或丢失、重
复和交换事件 [1]-[8]。事件日志中的错误阻碍了从事件日志分析中提取有用见
解的可能性。例如，它们明显影响通过流程发现获得的模型和一致性检查中的适
应度计算。因此，在运行事件日志分析之前，应该着眼于消除这些错误。
If a process model is available or can be discovered from clean traces, then errors can
be detected using traditional conformance checking techniques. However, in many
cases a process model is not available. Event log anomaly detection or cleaning has
emerged recently as a new field in process mining developing techniques to identify
anomalies in event logs without assuming the existence of a process model [1].
如果流程模型可用或可以从干净的轨迹中发现，则可以使用传统的一致性检查技
术检测错误。但是，在许多情况下，流程模型不可用。事件日志异常检测或清理
最近作为流程挖掘开发技术的一个新领域出现，用于在不假设流程模型存在的情
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