437 citations · 689 across the 19 of their papers we have counts for
20 papers
Agentic Business Process Management: A Research Manifesto
Diego Calvanese, Angelo Casciani, Giuseppe De Giacomo +15
This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for gove…
Evaluating the Process Modeling Abilities of Large Language Models -- Preliminary Foundations and Results
Peter Fettke, Constantin Houy
Large language models (LLM) have revolutionized the processing of natural language. Although first benchmarks of the process modeling abilities of LLM are promising, it is currentl…
Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review
Alexander Michael Rombach, Peter Fettke
Extracting key information from documents represents a large portion of business workloads and therefore offers a high potential for efficiency improvements and process automation.…
Interpretable and Explainable Machine Learning Methods for Predictive Process Monitoring: A Systematic Literature Review
Nijat Mehdiyev, Maxim Majlatow, Peter Fettke
This paper presents a systematic literature review (SLR) on the explainability and interpretability of machine learning (ML) models within the context of predictive process mining,…
A Discussion on Generalization in Next-Activity Prediction
Luka Abb, Peter Pfeiffer, Peter Fettke +1
Next activity prediction aims to forecast the future behavior of running process instances. Recent publications in this field predominantly employ deep learning techniques and eval…
Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective
Nijat Mehdiyev, Maxim Majlatow, Peter Fettke
This paper introduces a comprehensive, multi-stage machine learning methodology that effectively integrates information systems and artificial intelligence to enhance decision-maki…
Communicating Uncertainty in Machine Learning Explanations: A Visualization Analytics Approach for Predictive Process Monitoring
Nijat Mehdiyev, Maxim Majlatow, Peter Fettke
As data-driven intelligent systems advance, the need for reliable and transparent decision-making mechanisms has become increasingly important. Therefore, it is essential to integr…
Towards Automated Process Planning and Mining
Peter Fettke, Alexander Rombach
AI Planning, Machine Learning and Process Mining have so far developed into separate research fields. At the same time, many interesting concepts and insights have been gained at t…
Breathing Life into Models: The Next Generation of Enterprise Modeling
Peter Fettke, Wolfgang Reisig
Edsger W. Dijkstra has frequently suggested building a "firewall" between the technology- and application-side of computer science. His justification: The methods to attack the com…
Discrete models of continuous behavior of collective adaptive systems
Peter Fettke, Wolfgang Reisig
Artificial ants are "small" units, moving autonomously on a shared, dynamically changing "space", directly or indirectly exchanging some kind of information. Artificial ants are fr…
Towards Axiomatic Foundations for Conceptual Modeling: An Example
Peter Fettke, Wolfgang Reisig
Conceptual modeling is a strongly interdisciplinary field of research. Although numerous proposals for axiomatic foundations of the main ideas of the field exist, there is still a…
Modularization, Composition, and Hierarchization of Petri Nets with Heraklit
Peter Fettke, Wolfgang Reisig
It is known for decades that computer-based systems cannot be understood without a concept of modularization and decomposition. We suggest a universal, expressive, intuitively attr…
Systems Mining with Heraklit: The Next Step
Peter Fettke, Wolfgang Reisig
We suggest systems mining as the next step after process mining. Systems mining starts with a more careful investigation of runs, and constructs a detailed model of behavior, more…
Multivariate Business Process Representation Learning utilizing Gramian Angular Fields and Convolutional Neural Networks
Peter Pfeiffer, Johannes Lahann, Peter Fettke
Learning meaningful representations of data is an important aspect of machine learning and has recently been successfully applied to many domains like language understanding or com…
A systematic literature review on state-of-the-art deep learning methods for process prediction
Dominic A. Neu, Johannes Lahann, Peter Fettke
Process mining enables the reconstruction and evaluation of business processes based on digital traces in IT systems. An increasingly important technique in this context is process…
Modelling service-oriented systems and cloud services with Heraklit
Peter Fettke, Wolfgang Reisig
Modern and next generation digital infrastructures are technically based on service oriented structures, cloud services, and other architectures that compose large systems from sma…
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring
Nijat Mehdiyev, Peter Fettke
The contemporary process-aware information systems possess the capabilities to record the activities generated during the process execution. To leverage these process specific fine…
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing
Nijat Mehdiyev, Peter Fettke
This study proposes an innovative explainable predictive quality analytics solution to facilitate data-driven decision-making for process planning in manufacturing by combining pro…
XES Tensorflow - Process Prediction using the Tensorflow Deep-Learning Framework
Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke
Predicting the next activity of a running process is an important aspect of process management. Recently, artificial neural networks, so called deep-learning approaches, have been…
Predicting Process Behaviour using Deep Learning
Joerg Evermann, Jana-Rebecca Rehse, Peter Fettke
Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an applicati…