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

In this work we propose an algorithm for trace recovery from stochastically
known logs, a setting that is becoming more common with the increasing number
of sensors and predictive models that generate uncertain data. The suggested
approach calculates the conformance between a process model and a
stochastically known trace and recovers the best alignment within this
stochastic trace as the true trace. The paper offers an analysis of the impact
of various cost models on trace recovery accuracy and makes use of a product
multi-graph to compare alternative trace recovery options. The average accuracy
of our approach, evaluated using two publicly available datasets, is
impressive, with an average recovery accuracy score of 90-97%, significantly
improving a common heuristic that chooses the most likely value for each
uncertain activity. We believe that the effectiveness of the proposed algorithm
in recovering correct traces from stochastically known logs may be a powerful
aid for developing credible decision-making tools in uncertain settings.

本文提出了一种从随机已知日志中恢复跟踪的算法，该方法基于过程模型计算跟踪与日志的符合度，并利用产品多图比较替代的跟踪恢复方案。在两个公开数据集上的平均精度为 90-97％，在这些不确定性数据的不确定环境中提供了正确轨迹恢复的有效算法。

随机已知日志的迹线恢复

Trace Recovery from Stochastically Known Logs

Machine learning is an established and frequently used technique in industry
and academia but a standard process model to improve success and efficiency of
machine learning applications is still missing. Project organizations and
machine learning practitioners have a need for guidance throughout the life
cycle of a machine learning application to meet business expectations. We
therefore propose a process model for the development of machine learning
applications, that covers six phases from defining the scope to maintaining the
deployed machine learning application. The first phase combines business and
data understanding as data availability oftentimes affects the feasibility of
the project. The sixth phase covers state-of-the-art approaches for monitoring
and maintenance of a machine learning applications, as the risk of model
degradation in a changing environment is eminent. With each task of the
process, we propose quality assurance methodology that is suitable to adress
challenges in machine learning development that we identify in form of risks.
The methodology is drawn from practical experience and scientific literature
and has proven to be general and stable. The process model expands on CRISP-DM,
a data mining process model that enjoys strong industry support but lacks to
address machine learning specific tasks. Our work proposes an industry and
application neutral process model tailored for machine learning applications
with focus on technical tasks for quality assurance.

我们提出了一个适用于机器学习应用程序的过程模型，该模型涵盖从定义范围到维护部署的机器学习应用程序的六个阶段，其中每个阶段都包括适合解决风险挑战的质量保障方法，是一个具有普适性和稳定性的质量保障技术任务的行业和应用程序中性过程模型，该模型扩展了数据挖掘过程模型 CRISP-DM，但缺乏解决机器学习特定任务的能力。