← Back to all news
arXiv AI

Decision-Aware Suffix Prediction and Reasoning of Business Processes

The paper introduces a decision‑aware suffix prediction framework that combines neural suffix predictors with decision mining rules extracted from event logs. By incorporating case‑ and event‑level attributes into the prediction process, the approach improves accuracy for short prefixes and rare process variants. Experiments on multiple event logs demonstrate that the framework not only enhances prediction performance but also provides intrinsic interpretability through mined decision rules.

Related stories

arXiv Machine Learning
Oct 23

What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules

The paper introduces RULES, a neuro-symbolic model that learns sequential rules directly from data, and RUNES, which combines these rules with a residual network for next-event prediction. RULES can accurately recover ground truth rules in synthetic experiments and uncover compact, interpretable rule sets on real datasets. RUNES achieves competitive accuracy while offering transparent access to the active rules and the residual adjustments that refine predictions.

By Tim Nico Bauerschmidt, Isabel Valera, Jilles Vreeken
safety
More like this →
arXiv Machine Learning
Oct 21

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

The paper introduces SiamSA-PPM, a self‑supervised framework that augments event logs for predictive process monitoring by generating realistic trace variants using control‑flow semantics and frequent behavioral patterns. It combines Siamese learning with statistically grounded data augmentation to learn generalizable prefix representations without requiring labeled data. Experiments on diverse real‑world logs show that SiamSA-PPM matches or outperforms state‑of‑the‑art baselines in next‑activity and final‑outcome prediction tasks.

By Sjoerd van Straten, Alessandro Padella, Marwan Hassani
benchmarks
More like this →
arXiv AI
Sep 2

Automated Event Log Generation from Unstructured Text Using Finetuned LLMs

The paper introduces a scalable framework that uses finetuned large language models (LLMs) to translate unstructured textual resources into structured event logs for process mining. By creating a new text-to-log dataset and finetuning LLMs on it, the authors demonstrate that the resulting models produce high‑fidelity event logs, outperforming few‑shot or zero‑shot prompting methods. This approach enables previously unused organizational data, such as incident tickets and manuals, to be incorporated into process mining workflows.

By Maximilian Seeth, Gabriel Marques Tavares, Daniel Schuster
llmsfine-tuning
More like this →
arXiv AI
Aug 28

Learning to Predict, Discover, and Reason in High-Dimensional Event Sequences

The paper proposes a new framework for automated fault diagnostics in modern vehicles by treating diagnostic trouble codes (DTCs) as a high‑dimensional language. It introduces Transformer‑based models for predictive maintenance, scalable causal discovery methods, and a multi‑agent system that automatically generates Boolean error‑pattern rules. The approach aims to replace costly manual grouping of DTCs with scalable, data‑driven techniques.

By Hugo Math
llmsagentssafety
More like this →
arXiv AI
Oct 26

Learning to Predict, Discover, and Reason in High-Cardinality Event Sequences

The article discusses a new approach to diagnosing vehicle faults by treating diagnostic trouble codes (DTCs) as a high‑cardinality language. It proposes Transformer‑based models for predicting event sequences, scalable causal discovery frameworks, and a multi‑agent system that automatically generates Boolean error‑pattern rules. These methods aim to replace manual, error‑prone grouping of DTCs with automated, scalable fault diagnostics.

By Hugo Math
llmsagentssafety
More like this →
arXiv Machine Learning
Jul 31

Revisiting Predictive Process Monitoring in the Age of Foundation Models: A Comparative Study of Sequence, Tabular, and LLM Approaches

arXiv:2607. 27797v1 Announce Type: new Abstract: Predictive process monitoring (PPM) leverages event logs to forecast the future of running process instances, for instance, predicting the next activity, the remaining time until case completion, or the time to the next event.

By Lennart Fertig, Lukas Kirchdorfer, Tobias Sesterhenn
llmsbenchmarks
More like this →