49 citations · 50 across the 11 of their papers we have counts for
11 papers
Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes
Jiaxin Yuan, Daniela Grigori, Han van der Aa
Operators of service-based systems act on forecasts of how a running execution will continue, and such a forecast is actionable only if its reliability is known. Mainstream deep-le…
From Global Policies to Local Strategies: Multi-Objective Optimization of Resource-Specific Handover Policies
Lukas Kirchdorfer, Artemis Doumeni, Han van der Aa +1
Efficient resource allocation is a key challenge in business process management, with direct implications for cost, throughput time, and utilization. While recent Reinforcement Lea…
What is Business Process Automation Anyway?
Hoang Vu, Henrik Leopold, Han van der Aa
Many organizations strive to increase the level of automation in their business processes. While automation historically was mainly concerned with automating physical labor, curren…
Assisted Data Annotation for Business Process Information Extraction from Textual Documents
Julian Neuberger, Han van der Aa, Lars Ackermann +3
Machine-learning based generation of process models from natural language text process descriptions provides a solution for the time-intensive and expensive process discovery phase…
AgentSimulator: An Agent-based Approach for Data-driven Business Process Simulation
Lukas Kirchdorfer, Robert Blümel, Timotheus Kampik +2
Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first pe…
A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models
Julian Neuberger, Lars Ackermann, Han van der Aa +1
Over the past decade, extensive research efforts have been dedicated to the extraction of information from textual process descriptions. Despite the remarkable progress witnessed i…
Mining Constraints from Reference Process Models for Detecting Best-Practice Violations in Event Logs
Adrian Rebmann, Timotheus Kampik, Carl Corea +1
Detecting undesired process behavior is one of the main tasks of process mining and various conformance-checking techniques have been developed to this end. These techniques typica…
PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
Keyvan Amiri Elyasi, Han van der Aa, Heiner Stuckenschmidt
We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remai…
From OCEL to DOCEL -- Datasets and Automated Transformation
Alexandre Goossens, Adrian Rebmann, Johannes De Smedt +2
Object-centric event data represent processes from the point of view of all the involved object types. This perspective has gained interest in recent years as it supports the analy…
Large Process Models: A Vision for Business Process Management in the Age of Generative AI
Timotheus Kampik, Christian Warmuth, Adrian Rebmann +12
The continued success of Large Language Models (LLMs) and other generative artificial intelligence approaches highlights the advantages that large information corpora can have over…
GECCO: Constraint-driven Abstraction of Low-level Event Logs
Adrian Rebmann, Matthias Weidlich, Han van der Aa
Process mining enables the analysis of complex systems using event data recorded during the execution of processes. Specifically, models of these processes can be discovered from e…