From Process-Agnostic to Process-Aware Automation, Mining, and Prediction

Stefanie Rinderle-Ma, Janik Vasily Benzin, Juergen Mangler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

The entire research area of (business) process management has experienced a tremendous push with the advent of process mining, robotic process automation, and predictive process monitoring. While this development is highly appreciated, the current process-agnostic pipelines for process mining, robotic process automation, and predictive process monitoring have several limitations. Taking a system perspective, this keynote elaborates the limitations of process-agnostic automation. Then, it shows how a shift towards process-aware automation and predictive compliance monitoring can be achieved and how process-aware pipelines contribute to overcome the limitations of process-agnostic automation. Finally, research implications with a focus on Petri nets are derived.

Original languageEnglish
Title of host publicationApplication and Theory of Petri Nets and Concurrency - 44th International Conference, PETRI NETS 2023, Proceedings
EditorsLuis Gomes, Robert Lorenz
PublisherSpringer Science and Business Media Deutschland GmbH
Pages3-15
Number of pages13
ISBN (Print)9783031336195
DOIs
StatePublished - 2023
Event44th International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2023 - Lisbon, Portugal
Duration: 25 Jun 202330 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13929 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference44th International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2023
Country/TerritoryPortugal
CityLisbon
Period25/06/2330/06/23

Keywords

  • Predictive Compliance Monitoring
  • Predictive Process Monitoring
  • Process Automation
  • Process Mining

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