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Found 7 papers, 1 papers with code
The Imprecisions of Precision Measures in Process Mining
In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log.
Scalable Alignment of Process Models and Event Logs: An Approach Based on Automata and S-Components
To address this limitation, the paper proposes a second technique wherein the process model is first decomposed into a set of automata, known as S-components, such that the product of these automata is equal to the automaton of the whole process model.
Software Engineering
Inferring Unobserved Events in Systems With Shared Resources and Queues
To understand and analyze the behavior and performance of processes with shared resources, we aim to reconstruct bounds for timestamps of events in a case that must have happened but were not recorded by inference over events in other cases in the system.
Process Discovery Using Graph Neural Networks
In this paper, we explore the problem of supervised learning of a process discovery technique D. We introduce a technique for training an ML-based model D using graph convolutional neural networks; D translates a given input event log into a sound Petri net.
AI-Augmented Business Process Management Systems: A Research Manifesto
AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology.
Chit-Chat or Deep Talk: Prompt Engineering for Process Mining
This research investigates the application of Large Language Models (LLMs) to augment conversational agents in process mining, aiming to tackle its inherent complexity and diverse skill requirements.
How well can large language models explain business processes?
The functionality of these services is to elicit the various knowledge ingredients that underlie SAX explanations.
