Traditional process mining techniques take event data as input where each
event is associated with exactly one object. An object represents the
instantiation of a process. Object-centric event data contain events associated
with multiple objects expressing the interaction of multiple processes. As
traditional process mining techniques assume events associated with exactly one
object, these techniques cannot be applied to object-centric event data. To use
traditional process mining techniques, the object-centric event data are
flattened by removing all object references but one. The flattening process is
lossy, leading to inaccurate features extracted from flattened data.
Furthermore, the graph-like structure of object-centric event data is lost when
flattening. In this paper, we introduce a general framework for extracting and
encoding features from object-centric event data. We calculate features
natively on the object-centric event data, leading to accurate measures.
Furthermore, we provide three encodings for these features: tabular,
sequential, and graph-based. While tabular and sequential encodings have been
heavily used in process mining, the graph-based encoding is a new technique
preserving the structure of the object-centric event data. We provide six use
cases: a visualization and a prediction use case for each of the three
encodings. We use explainable AI in the prediction use cases to show the
utility of both the object-centric features and the structure of the sequential
and graph-based encoding for a predictive model.

本文介绍了从对象中心事件数据中提取和编码特征的通用框架，提供了表格、序列和基于图形的编码，用于预测模型的实用性。

从以对象为中心的事件数据中提取和编码特征的框架

A Framework for Extracting and Encoding Features from Object-Centric Event Data

This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme,
a.k.a. GATES, to improve the predictor-based neural architecture search.
Specifically, different from existing graph-based schemes, GATES models the
operations as the transformation of the propagating information, which mimics
the actual data processing of neural architecture. GATES is a more reasonable
modeling of the neural architectures, and can encode architectures from both
the "operation on node" and "operation on edge" cell search spaces
consistently. Experimental results on various search spaces confirm GATES's
effectiveness in improving the performance predictor. Furthermore, equipped
with the improved performance predictor, the sample efficiency of the
predictor-based neural architecture search (NAS) flow is boosted. Codes are
available at this https URL

提出一种新型的基于图的神经架构编码方案 GATES，通过模拟神经架构的实际数据处理方式 -- 传播信息的转换，改进了基于预测器的神经架构搜索，并成功地提高了搜索样本的效率。