Predictive Process Monitoring

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Predictive process monitoring studies how to use an ongoing process’s event log—its activities, timestamps, and related data—to predict outcomes such as the next activity, remaining time, or likely completion sequence. Common approaches range from frequency-based and classical machine-learning models to LSTMs, Transformers, graph neural networks, and probabilistic sequence models; some also incorporate process rules or information about related cases. These forecasts can support earlier intervention in settings such as business workflows, clinical pathways, and industrial operations. Current work explores changing processes, rare and delayed cases, uncertainty, and richer context, while comparisons show that complex or foundation models do not consistently beat simple baselines.

Latest papers

25 papers3 papers in the last four weeks, down 62% on the four weeks before. 0.0% of all new papers.

Process-Aware Event Log Augmentation for Process Prediction under Labeled Data Scarcity
Oct 17, 2026Sjoerd van Straten, Alessandro Padella, Marwan HassaniPredictive Process Monitoring Process Mining
Eindhoven University of Technology, Eindhoven, The Netherlands · University of Padua, Padua, Italy · University of Wuppertal, Wuppertal, Germany
Foundation Models for Event Prediction Across Heterogeneous Event Logs
Oct 13, 2026Fabian Spaeh, Sean Jaffe, Shandian Zhe +1Next-Event Prediction Predictive Process Monitoring
Celonis
A Data-Driven Framework for Unsupervised Monitoring of Transmission Systems Using End-of-Line Testing Data: A Case Study at Ford Motor Company
Oct 4, 2026Mohammad N. Bisheh, Mehrdad Moradi, Parinaz Farajiparvar +6Industrial Anomaly Detection Time-Series Anomaly Detection
Georgia Tech, Atlanta, Georgia, USA · Ford Motor Company, Dearborn, Michigan, USA
Universal Multi-Modal Traceformer: Integrating Heterogeneous Context for Process Event Prediction
Sep 21, 2026Fabian Spaeh, Jingxing Fang, Shandian Zhe +1Event Time Prediction Predictive Process Monitoring
Celonis
Probabilistic Forecasting of Business Process Executions with Neural Temporal Point Processes
Sep 18, 2026Jiaxin Yuan, Daniela Grigori, Han van der AaProbabilistic Forecasting Predictive Process Monitoring
Paris Dauphine University-PSL, Pl. du Maréchal de Lattre de Tassigny, 75016 Paris, France · University of Vienna, Universitätsring 1, 1010 Wien, Austria
Complete Suffix Prediction for Recommendation via Latent Retrieval over Process Graphs
Sep 14, 2026Sarra Madad, Myriam Maumy, Fr{é}d{é}ric Bertrand +1Predictive Process Monitoring Process Mining
UTT, LIST3N - OPTI, QAD · EHESP, LIST3N - OPTI, ARENES · CEDRIC, LIST3N - OPTI +1
GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks
Sep 13, 2026Ana Costa, Johannes Mäkelburg, Luise PufahlPredictive Process Monitoring Process Mining
Technical University of Munich, School of Computation, Information and Technology, Heilbronn, Germany
On the Potential of Multi-Task Learning in Predictive Process Monitoring
Sep 11, 2026Lukas Kirchdorfer, Keyvan Amiri Elyasi, Heiner StuckenschmidtPredictive Process Monitoring Multi-Task Learning
University of Mannheim
Leveraging contextual events on structure-aware next activity prediction
Sep 8, 2026Alessandro Mele, Claudia Diamantini, Domenico PotenaPredictive Process Monitoring Process Mining
Department of Information Engineering, Polytechnic University of Marche, Ancona, Marche, Italy
IPM-FM: A Foundation Model with Consensus Feature Selection for Industrial Process Monitoring
Sep 8, 2026Liang Cao, Weide Liu, Yan Qin +3Predictive Process Monitoring Process Supervision
Department of Chemical and Biological Engineering University of British Columbia Vancouver, BC, Canada · School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang, China · School of Automation Chongqing University Chongqing, China +2
Decision-Aware Suffix Prediction and Reasoning of Business Processes
Sep 5, 2026Henryk Mustroph, Stefanie Rinderle-MaPredictive Process Monitoring Process Mining
Technical University of Munich, TUM School of Computation, Information and Technology, Garching, Germany
Efficient Online Continual Foundation Model Fine-Tuning for Predictive Process Monitoring
Aug 28, 2026Sjoerd van Straten, Marwan HassaniPredictive Process Monitoring Process Mining
Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands · Department of Mathematics and Computer Science, University of Wuppertal, Germany
D-TAIA: Domain-Aware LLM Adaptation for Multi-Task Predictive Process Monitoring
Aug 28, 2026Sjoerd van Straten, Christine Jacob, Marwan HassaniPredictive Process Monitoring Domain Adaptation
Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands · Department of Mathematics and Computer Science, University of Wuppertal, Germany
A Framework for Object-Centric Predictive Monitoring of Collaborative Processes
Aug 27, 2026Daniel Calegari, Andrea Delgado, Leonel Peña +1Process Mining Predictive Process Monitoring
Universidad ORT Uruguay, Cuareim 1451, Montevideo, 11100, Montevideo, Uruguay · Instituto de Computación, Facultad de Ingeniería, Universidad de la República, J. Herrera y Reissig 565, Montevideo, 11300, Montevideo, Uruguay
Mind the Long Tail: Understanding the Difficulty of Delay Detection in Business Processes
Aug 14, 2026Keyvan Amiri Elyasi, Lukas Kirchdorfer, Heiner StuckenschmidtPredictive Process Monitoring Process Mining
Data and Web Science Group, University of Mannheim, Germany · SAP Signavio, Walldorf, Germany
Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches
Jul 30, 2026Lennart Fertig, Lukas Kirchdorfer, Tobias SesterhennTabular Foundation Models Predictive Process Monitoring
University of Mannheim, Mannheim, Germany · SAP Signavio, Walldorf, Germany · Technical University of Clausthal, Clausthal, Germany
Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring
Jul 20, 2026Kseniya Sahatova, Rafael Seidi Oyamada, Xuefei Lu +1Feature Attribution Predictive Process Monitoring
SKEMA Business School, 92156 Suresnes, France · KU Leuven, B-3000 Leuven, Belgium
Graph Grounded Cross Attention Transformer Neural Network for Structurally Constrained Full Event Sequence Generation in Predictive Process Monitoring
Jun 17, 2026Fang Wang, Ernesto DamianiPredictive Process Monitoring Process Mining
Department of Computer Science, University of Milan, Via Giovanni Celoria, 18, Milano 20133, MI, Italy
David vs. Goliath in Next Activity Prediction: Argmax vs. LSTM, Transformer, and LLM
Jun 14, 2026Hans Weytjens, Ingo WeberLLM Evaluation Predictive Process Monitoring
Technical University of Munich, School of CIT, Germany · Fraunhofer Gesellschaft, Munich, Germany
From Data Lifting to Continuous Risk Estimation: A Process-Aware Pipeline for Predictive Monitoring of Clinical Pathways
May 5, 2026Pasquale Ardimento, Mario Luca Bernardi, Marta Cimitile +1Predictive Process Monitoring Process Mining
Department of Informatics, University of Bari Aldo Moro, Bari, Italy · Department of Engineering, University of Sannio, Benevento, Italy · Department of Economy and Law, UnitelmaSapienza University, Rome, Italy
A Multi-head Attention Fusion Network for Industrial Prognostics under Discrete Operational Conditions
Apr 11, 2026Yuqi Su, Xiaolei FangRUL Estimation Predictive Maintenance
Operations Research Graduate Program, North Carolina State University, Raleigh, NC, USA. · Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA.
Chameleons do not Forget: Prompt-Based Online Continual Learning for Next Activity Prediction
Apr 1, 2026Marwan Hassani, Tamara Verbeek, Sjoerd van StratenNext-Action Prediction Predictive Process Monitoring
Department of Mathematics and Computer Science, Eindhoven University of Technology, The Netherlands
Compliance-Aware Predictive Process Monitoring: A Neuro-Symbolic Approach
Mar 27, 2026Fabrizio De Santis, Gyunam Park, Wil M. P. van der Aalst +1Logic Tensor Networks Predictive Process Monitoring
University of Parma, Parma, Italy · Eindhoven University of Technology, Eindhoven, Netherlands · RWTH Aachen University, Aachen, Germany
Neuro-Symbolic Learning for Predictive Process Monitoring via Two-Stage Logic Tensor Networks with Rule Pruning
Mar 27, 2026Fabrizio De Santis, Gyunam Park, Francesco ZanichelliLogic Tensor Networks Predictive Process Monitoring
University of Parma, Parma, Italy · Eindhoven University of Technology, Eindhoven, Netherlands
Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM
Mar 24, 2026Hyunwoo Kim, Munyoung Lee, Seung Hyub Jeon +1Semiconductor Manufacturing Predictive Process Monitoring
Electronics and Telecommunications Research Institute, Daejeon, Republic of Korea

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Process-Aware Event Log Augmentation for Process Prediction under Labeled Data Scarcity

Sjoerd van Straten, Alessandro Padella, Marwan Hassani

Eindhoven University of Technology, Eindhoven, The Netherlands · University of Padua, Padua, Italy · University of Wuppertal, Wuppertal, Germany

Predictive Process Monitoring Process Mining Data Augmentation Synthetic Data Augmentation Self-Supervised Learning

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Predictive Process Monitoring (PPM) forecasts future events of ongoing business processes based on event logs. However, deep learning PPM approaches are often limited by labeled data scarcity and the low variability of real-world logs. To address this, we introduce SiamSA-PPM, a self-supervised framework combining Siamese learning with statistically grounded data augmentation. It employs novel transformation methods that leverage control-flow semantics and frequent behavioral patterns to generate realistic, semantically valid trace variants, learning generalizable prefix representations without labeled supervision. Extensive experiments on real-world event logs with a wide range of variability, incorporating exhaustive hyperparameter tuning, demonstrate the value of this process-aware augmentation. Compared to state-of-the-art baselines, SiamSA-PPM achieves competitive or superior predictive performance in both next activity and final outcome prediction tasks. An open source implementation of our method is publicly available under https://github.com/SvStraten/SiamSA-PPM-Extension..