Search Results for author: Dirk Fahland

Found 5 papers, 0 papers with code

Augmented Business Process Management Systems: A Research Manifesto

no code implementations30 Jan 2022 Marlon Dumas, Fabiana Fournier, Lior Limonad, Andrea Marrella, Marco Montali, Jana-Rebecca Rehse, Rafael Accorsi, Diego Calvanese, Giuseppe De Giacomo, Dirk Fahland, Avigdor Gal, Marcello La Rosa, Hagen Völzer, Ingo Weber

Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems that draws upon trustworthy AI technology.

Process Discovery Using Graph Neural Networks

no code implementations13 Sep 2021 Dominique Sommers, Vlado Menkovski, Dirk Fahland

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.

Inferring Unobserved Events in Systems With Shared Resources and Queues

no code implementations27 Feb 2021 Dirk Fahland, Vadim Denisov, Wil. M. P. van der Aalst

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.

Scalable Alignment of Process Models and Event Logs: An Approach Based on Automata and S-Components

no code implementations22 Oct 2019 Daniel Reißner, Abel Armas-Cervantes, Raffaele Conforti, Marlon Dumas, Dirk Fahland, Marcello La Rosa

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

The Imprecisions of Precision Measures in Process Mining

no code implementations3 May 2017 Niek Tax, Xixi Lu, Natalia Sidorova, Dirk Fahland, Wil M. P. van der Aalst

In process mining, precision measures are used to quantify how much a process model overapproximates the behavior seen in an event log.

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