Causal Process Mining from Relational Databases with Domain Knowledge

The plethora of algorithms in the research field of process mining builds on directly-follows relations. Even though various improvements have been made in the last decade, there are serious weaknesses of these relationships. Once events associated with different objects that relate with a cardinali...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Waibel, Philipp, Pfahlsberger, Lukas, Revoredo, Kate, Mendling, Jan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.07.2023
Subjects
Algorithms
Data mining
Data structures
Relational data bases
Online AccessGet full text

Cover

More Information
Summary:The plethora of algorithms in the research field of process mining builds on directly-follows relations. Even though various improvements have been made in the last decade, there are serious weaknesses of these relationships. Once events associated with different objects that relate with a cardinality of 1:N and N:M to each other, techniques based on directly-follows relations produce spurious relations, self-loops, and back-jumps. This is due to the fact that event sequence as described in classical event logs differs from event causation. In this paper, we address the research problem of representing the causal structure of process-related event data. To this end, we develop a new approach called Causal Process Mining. This approach renounces the use of flat event logs and considers relational databases of event data as an input. More specifically, we transform the relational data structures based on the Causal Process Template into what we call Causal Event Graph. We evaluate our approach and compare its outputs with techniques based on directly-follows relations in a case study with an European food production company. Our results demonstrate the benefits for enriching process mining with additional knowledge from the domain.
ISSN:2331-8422