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

Adams, Jan Niklas; Park, Gyunam; Levich, Sergej; Schuster, Daniel; Aalst, Wil M. P. van der

doi:10.1007/978-3-031-20984-0_3

Cited by 11 publications

(1 citation statement)

References 27 publications

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“…The graph-related properties of these events and objects are then used to identify events/objects with similar behavior (this is eventually called variant). These properties are also exploited in [11] for feature extraction on object-centric event logs. Different use cases (including predictive analytics) and types of feature extraction (table-based, graph-based) are proposed.…”

Section: Related Workmentioning

confidence: 99%

Graph-Based Feature Extraction on Object-Centric Event Logs

Berti

Herforth

Qafari

et al. 2022

Preprint

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Process mining techniques are widely used to uncover performance and compliance problems. However, the traditional focus on a single object type (i.e., case) is a limiting factor when considering real-life information systems. Therefore, there is an increased interest in object-centric process mining. This paper proposes a graph-based approach for feature extraction on object-centric event logs. The conversion of the event log to a set of numeric vectors is the starting point for the application of any machine learning technique (classification, prediction, clustering, anomaly detection) on top of the event data. However, while feature extraction on traditional event logs is established, object-centric event logs are more difficult to encode in numeric features because the events are related to several interconnected objects. Here, we try to close this gap and propose techniques and tools implementing feature extraction on object-centric event logs. The usefulness of the proposed features is discussed on top of four problems in a Procure-to-Pay process.

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Section: Related Workmentioning

confidence: 99%

Graph-Based Feature Extraction on Object-Centric Event Logs

Berti

Herforth

Qafari

et al. 2022

Preprint

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Trace Encoding Techniques for Multi‐Perspective Process Mining: A Comparative Study

Rullo,

Alam,

Serra

2024

WIREs Data Min & Knowl

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Process mining (PM) comprises a variety of methods for discovering information about processes from their execution logs. Some of them, such as trace clustering, trace classification, and anomalous trace detection require a preliminary preprocessing step in which the raw data is encoded into a numerical feature space. To this end, encoding techniques are used to generate vectorial representations of process traces. Most of the PM literature provides trace encoding techniques that look at the control flow, that is, only encode the sequence of activities that characterize a process trace disregarding other process data that is fundamental for effectively describing the process behavior. To fill this gap, in this article we show 19 trace encoding methods that work in a multi‐perspective manner, that is, by embedding events and trace attributes in addition to activity names into the vectorial representations of process traces. We also provide an extensive experimental study where these techniques are applied to real‐life datasets and compared to each other.

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