Next-Activity Prediction for Non-stationary Processes with Unseen Data Variability

Mangat, Amolkirat Singh; Rinderle-Ma, Stefanie

doi:10.1007/978-3-031-17604-3_9

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…"This uncertainty often manifests itself in significant changes in the executed processes" [5]. Process changes, in turn, manifest as concept drifts in process event logs [5] and as unseen behavior in process event streams [23]. A selection of use cases for process changes from different domains can be found in [17].…”

Section: Process Miningmentioning

confidence: 99%

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

Rinderle-Ma

Benzin

Mangler

2023

Lecture Notes in Computer Science

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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.

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