Event log records all events that occur during the execution of business
processes, so detecting and correcting anomalies in event log can provide
reliable guarantee for subsequent process analysis. The previous works mainly
include next event prediction based methods and autoencoder-based methods.
These methods cannot accurately and efficiently detect anomalies and correct
anomalies at the same time, and they all rely on the set threshold to detect
anomalies. To solve these problems, we propose a business process anomaly
correction method based on Transformer autoencoder. By using self-attention
mechanism and autoencoder structure, it can efficiently process event sequences
of arbitrary length, and can directly output corrected business process
instances, so that it can adapt to various scenarios. At the same time, the
anomaly detection is transformed into a classification problem by means of
selfsupervised learning, so that there is no need to set a specific threshold
in anomaly detection. The experimental results on several real-life event logs
show that the proposed method is superior to the previous methods in terms of
anomaly detection accuracy and anomaly correction results while ensuring high
running efficiency.

基于 Transformer 自编码器的业务过程异常修正方法能够高效处理任意长度的事件序列，并且可以直接输出修正后的业务过程实例，从而适应各种场景，通过自监督学习将异常检测转化为分类问题，无需设置特定阈值，在确保高运行效率的同时，在异常检测准确性和异常修正结果方面优于以往方法。

使用 Transformer 自编码器进行业务流程的异常修正

Anomaly Correction of Business Processes Using Transformer Autoencoder

Process events are recorded by multiple information systems at different
granularity levels. Based on the resulting event logs, process models are
discovered at different granularity levels, as well. Events stored at a
fine-grained granularity level, for example, may hinder the discovered process
model to be displayed due the high number of resulting model elements. The
discovered process model of a real-world manufacturing process, for example,
consists of 1,489 model elements and over 2,000 arcs. Existing process model
abstraction techniques could help reducing the size of the model, but would
disconnect it from the underlying event log. Existing event abstraction
techniques do neither support the analysis of mixed granularity levels, nor
interactive exploration of a suitable granularity level. To enable the
exploration of discovered process models at different granularity levels, we
propose INEXA, an interactive, explainable process model abstraction method
that keeps the link to the event log. As a starting point, INEXA aggregates
large process models to a "displayable" size, e.g., for the manufacturing use
case to a process model with 58 model elements. Then, the process analyst can
explore granularity levels interactively, while applied abstractions are
automatically traced in the event log for explainability.

基于不同粒度级别的信息系统记录的过程事件，在生成的事件日志的基础上，也发现了不同粒度级别的过程模型。为了实现对不同粒度级别的发现的过程模型的可视化和交互探索，我们提出了 INEXA，一个交互式且可解释的过程模型抽象方法，保持与事件日志的联系。

INEXA: 通过面向对象的流程挖掘进行交互和可解释的流程模型抽象

INEXA: Interactive and Explainable Process Model Abstraction Through  Object-Centric Process Mining

Process mining allows analysts to exploit logs of historical executions of
business processes to extract insights regarding the actual performance of
these processes. One of the most widely studied process mining operations is
automated process discovery. An automated process discovery method takes as
input an event log, and produces as output a business process model that
captures the control-flow relations between tasks that are observed in or
implied by the event log. Various automated process discovery methods have been
proposed in the past two decades, striking different tradeoffs between
scalability, accuracy and complexity of the resulting models. However, these
methods have been evaluated in an ad-hoc manner, employing different datasets,
experimental setups, evaluation measures and baselines, often leading to
incomparable conclusions and sometimes unreproducible results due to the use of
closed datasets. This article provides a systematic review and comparative
evaluation of automated process discovery methods, using an open-source
benchmark and covering twelve publicly-available real-life event logs, twelve
proprietary real-life event logs, and nine quality metrics. The results
highlight gaps and unexplored tradeoffs in the field, including the lack of
scalability of some methods and a strong divergence in their performance with
respect to the different quality metrics used.

该论文通过对 12 个公开事件日志，12 个专有事件日志和 9 个质量指标的评估，对自动化流程发现方法进行了系统的回顾和比较评估，突出了该领域存在的差距和未被探索的权衡，其中包括某些方法缺乏可扩展性以及它们在所使用的不同质量度量方面性能强烈分歧。