A Framework for Extracting and Encoding Features from Object-Centric
Event Data
- URL: http://arxiv.org/abs/2209.01219v1
- Date: Fri, 2 Sep 2022 16:49:47 GMT
- Title: A Framework for Extracting and Encoding Features from Object-Centric
Event Data
- Authors: Jan Niklas Adams, Gyunam Park, Sergej Levich, Daniel Schuster, Wil
M.P. van der Aalst
- Abstract summary: We introduce a framework for extracting and encoding features from object-centric event data.
We calculate features on the object-centric event data, leading to accurate measures.
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.
- Score: 0.36748639131154304
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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