← 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 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 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 →
Hugging Face Trending Papers
Jul 20

Feature Attribution-Based Explainability Analysis of Deep Learning Models in Predictive Process Monitoring

Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural networks have achieved strong performance for these tasks by modeling sequential dependencies in event logs, their black-box nature limits trust and practical adoption.

computer-vision
More like this →
arXiv AI
Aug 26

Towards Reproducibility in Predictive Process Mining: SPICE -- A Deep Learning Library

The paper introduces SPICE, a Python framework that reimplements three popular deep‑learning methods for Predictive Process Mining (PPM) using PyTorch. It provides a common, highly configurable base to enable reproducible and robust comparison of PPM models, addressing issues of reproducibility, transparency, and usability. The authors benchmark SPICE against the original reported metrics and fair metrics across 11 datasets.

By Oliver Stritzel, Nick H\"uhnerbein, Simon Rauch, Itzel Zarate, Lukas Fleischmann, Moike Buck, Attila Lischka, Christian Frey
benchmarks
More like this →