Business Process Modeling

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16 papers0 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Concurrency-Aware Process Model Forecasting with Causal Nets
Sep 18, 2026Yongbo Yu, Jari Peeperkorn, Johannes De Smedt +1Process Mining Business Process Modeling
Research Center for Information Systems Engineering (LIRIS), KU Leuven, Leuven, Belgium
BPMN4CAI: A BPMN Extension for Modeling Dynamic Conversational AI
Aug 27, 2026Björn-Lennart Eger, Daniel Rose, Barbara DinterBusiness Process Modeling Conversational Agents
Chemnitz University of Technology, Chair of Business Information Systems - Business Process and Information Management, Chemnitz, Germany
A Hybrid LLM-Based Framework for Automated Security Annotation Generation in Business Process Models
Aug 14, 2026Md Kamrul Islam, Tiphaine Henry, Mattia Salnitri +2Business Process Modeling LLM-Assisted Annotation
CentraleSupélec, Gif-sur-Yvette, France · Université Paris-Saclay, Palaiseau, France · University of Bergamo, Bergamo, Italy +1
Cross-organisational Process Mining from Message Logs
Jul 29, 2026Pieter Kwantes, Jetty KleijnProcess Mining Business Process Modeling
LIACS, Leiden University, P.O.Box 9512 NL-2300 RA Leiden, The Netherlands
Improving LLM-Generated Process Model Quality Through Reinforcement Learning: The Role of Reward Function Design
Jul 7, 2026Alexander Rombach, Chantale Lauer, Nijat MehdiyevRL for Language Models Multi-Objective Language Model Alignment
German Research Center for Artificial Intelligence (DFKI), Campus D3 2, Saarbrücken, 66123, Saarland, Germany · Saarland University, Campus D3 2, Saarbrücken, 66123, Saarland, Germany
A Formal Framework for Declarative Agentic AI in Business Process Analysis
Jun 13, 2026Mohammad Azarijafari, Luisa Mich, Michele MissikoffBusiness Process Automation Knowledge Representation
Department of Industrial Engineering, University of Trento, Via Sommarive 9, 38123, Trento, Italy · Istituto di Analisi dei Sistemi ed Informatica (IASI) “Antonio Ruberti”, National Research Council (CNR), Via dei Taurini 19, 00185, Rome, Italy
Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Text
May 23, 2026Anton Antonov, Humam Kourani, Alessandro Berti +1Business Process Automation Business Process Modeling
Fraunhofer Institute for Applied Information Technology FIT, Schloss Birlinghoven, 53757 Sankt Augustin, Germany · RWTH Aachen University, Ahornstraße 55, 52074 Aachen, Germany
Pragmos: A Process Agentic Modeling System
Apr 30, 2026Pedro-Aarón Hernández-Ávalos, Luciano García-BañuelosAgentic Workflows Business Process Modeling
Tecnologico de Monterrey, Mexico
Task-Level AI Readiness Assessment for Business Process Management:The T-IPO Model and LARA Matrix in Financial-Services IT Operations
Apr 16, 2026Mingjun Li, Xiaojun YeLLM Agent Evaluation Multi-Criteria Decision Analysis
Tsinghua University, Beijing, China
Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends
Apr 15, 2026João Bettencourt, Sérgio GuerreiroBusiness Process Modeling
INESC-ID, R. Alves Redol 9, 1000-029 Lisbon, Portugal · Instituto Superior T´ecnico, Universidade de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal
Ambiguity Detection and Elimination in Automated Executable Process Modeling
Apr 13, 2026Ion Matei, Praveen Kumar Menaka Sekar, Maksym Zhenirovskyy +4Business Process Modeling Business Process Automation
Fujitsu Research of America · University of Maryland, College Park · Fujitsu Limited
Automatic Generation of Executable BPMN Models from Medical Guidelines
Apr 9, 2026Praveen Kumar Menaka Sekar, Ion Matei, Maksym Zhenirovskyy +4Business Process Modeling Business Process Automation
Dept. of Mechanical Engineering, University of Maryland, College Park, MD 20742, USA · Fujitsu Research of America, Santa Clara, CA 95054, USA · Fujitsu Limited, Nakahara-ku, Kawasaki, Kanagawa 211-8588, Japan
Discovery of Decision Synchronization Patterns from Event Logs
Mar 20, 2026Tijmen Kuijpers, Karolin Winter, Remco DijkmanBusiness Process Modeling Process Mining
Eindhoven University of Technology, Department of Industrial Engineering and Innovation Sciences, Eindhoven, The Netherlands
Human-Centered Evaluation of an LLM-Based Process Modeling Copilot: A Mixed-Methods Study with Domain Experts
Mar 13, 2026Chantale Lauer, Peter Pfeiffer, Nijat MehdiyevBusiness Process Modeling LLM Agent Evaluation
Saarland University, Saarbrücken, Saarland, Germany · German Research Institute for Artificial Intelligence, Saarbrücken, Saarland, Germany · 5Plus GmbH, Würzburg, Bavaria, Germany
Assessing the Business Process Modeling Competences of Large Language Models
Jan 29, 2026Chantale Lauer, Peter Pfeiffer, Alexander Rombach +1LLM Evaluation Business Process Modeling
German Research Center for Artificial Intelligence (DFKI), Campus D3 2, Saarbrücken, 66123, Germany · Saarland University, Campus D3 2, Saarbrücken, 66123, Germany · 5Plus GmbH, Goethestraße 1, Würzburg, 97072, Germany
Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs
May 24, 2025Chen Yang, Ruping Xu, Ruizhe Li +2LLM Grounding LLM Prompting
Zhejiang University of Technology, China1 · Zhejiang Key Laboratory of Visual Information Intelligent Processing, China 2 · University of Aberdeen, UK +1

Sep 18, 2026J/K move · Enter open · S save

Concurrency-Aware Process Model Forecasting with Causal Nets

Yongbo Yu, Jari Peeperkorn, Johannes De Smedt, Jochen De Weerdt

Research Center for Information Systems Engineering (LIRIS), KU Leuven, Leuven, Belgium

Process Mining Business Process Modeling

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Process model forecasting (PMF) aims to predict the process model that will characterize a future period, thereby providing a process-level view of how behavior is expected to evolve. Existing PMF methods, however, forecast directly-follows graphs, which cannot explicitly represent concurrency. We extend PMF to causal nets by forecasting time series of relation and binding counts and using these forecasts to reconstruct future process models with AND/XOR semantics. To evaluate the resulting models, we introduce a protocol that accounts for partial traces and constructs the workflow nets required for conformance checking. Experiments on four event logs show that the forecasted models achieve conformance levels close to those of models re-mined from observations in the corresponding future windows. They also outperform static discovery baselines, which retain high precision on the structurally stable log but exhibit substantial precision losses on the other three logs. Filtering infrequent bindings improves most conformance metrics, although it also removes much of the concurrent behavior captured by the models.