Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs.

Dirk Fahland

doi:10.1007/978-3-031-08848-3_9

Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs.

Dirk Fahland

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

34 Citations (Scopus)

Abstract

Classical process mining relies on the notion of a unique case identifier, which is used to partition event data into independent sequences of events. In this chapter, we study the shortcomings of this approach for event data over multiple entities. We introduce event knowledge graphs as data structure that allows to naturally model behavior over multiple entities as a network of events. We explore how to construct, query, and aggregate event knowledge graphs to get insights into complex behaviors. We will ultimately show that event knowledge graphs are a very versatile tool that opens the door to process mining analyses in multiple behavioral dimensions at once.

Original language	English
Title of host publication	Process Mining Handbook
Pages	274-319
Number of pages	46
DOIs	https://doi.org/10.1007/978-3-031-08848-3_9
Publication status	Published - 2022

Publication series

Name	Lecture Notes in Business Information Processing
Volume	448
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Bibliographical note

DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Keywords

Event knowledge graph
Process mining

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10.1007/978-3-031-08848-3_9Licence: CC BY

https://dblp.org/db/books/collections/PMH2022.html#Fahland22

Cite this

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PY - 2022

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M3 - Chapter

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BT - Process Mining Handbook

ER -

Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs.

Abstract

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Bibliographical note

Keywords

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