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
Process mining (PM) provides a powerful framework for discovering and optimizing operational processes from event data. However, the efficacy of PM techniques is strictly predicated on the availabilit...
arXiv:2609.14534v1 Announce Type: new
Abstract: Predictive Process Monitoring (PPM) aims at predicting at runtime and as early as possible the future states of a process execution. Common tasks inclu...
By Ana Costa, Johannes M\"akelburg, Luise Pufahl
arXiv:2603. 11479v3 Announce Type: replace-cross Abstract: Time Series Event Detection (TSED) aims to localize semantically meaningful events in time series data, with critical applications in high-stakes domains.
By Sky Chenwei Wan, Yifei Y. Wang, Tianjun Hou, Xiqing Chang, Aymeric Jan
AgileLog introduces a forkable shared log designed to support AI agents that interact with streaming data. The new abstraction provides forking primitives that allow agents to operate without causing performance interference or unsafe writes. Bolt is a system that implements AgileLog, employing techniques to keep forks inexpensive while ensuring logical and performance isolation.
By Shreesha G. Bhat, Tony Hong, Michael Noguera, Aishwarya Ganesan, Ramnatthan Alagappan
arXiv:2602. 17001v3 Announce Type: replace Abstract: Natural Language Querying for Time Series Databases (NLQ4TSDB) aims to assist non-expert users retrieve meaningful events, intervals, and summaries from massive temporal records.
By Zhao Tan, Yiji Zhao, Shiyu Wang, Chang Xu, Yuxuan Liang, Xiping Liu, Shirui Pan, Ming Jin
The paper introduces a scalable cross‑domain event extraction system built on a unified generative sequence‑to‑sequence framework. It jointly handles event detection and argument extraction, allowing both pipeline and end‑to‑end configurations. By fine‑tuning pretrained language models on multiple event datasets from diverse domains, the system retains domain‑specific semantics while generalizing across large, evolving label spaces, and offers a web‑based application for researchers to upload documents, extract events, visualize triggers and arguments, and compare configurations.
By Siting Liang, Omar Adjali, Omair Shahzad Bhatti, Daniel Sonntag
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
The paper introduces MACE, a Multi-Agent Candidate Event acquisition method designed to improve event linking by refining event structure before the linking step. MACE employs evidence-specialized large language model agents to gather time, location, participant, and event-type evidence, exposes intermediate queries to candidate-event lookup tools, and allows a coordinator to revise the evidence set before final candidate construction. Experiments on two event linking benchmarks demonstrate that integrating MACE consistently boosts accuracy across different event linking models without altering the underlying models.
By Ziyang Zhang, Yinan Liu, Boyi Xue, Yingxuan Huang, Bin Wang, Xiaochun Yang
arXiv:2609.24579v1 Announce Type: new
Abstract: Event logs arise in a wide range of real-world processes, capturing not only event activities and timestamps but also multi-modal contextual informatio...
By Fabian Spaeh, Jingxing Fang, Shandian Zhe, Bin Shen
arXiv:2308. 04214v2 Announce Type: replace-cross Abstract: In the domain of knowledge representation and reasoning within AI, datalog engines play an ever-increasingly crucial role.
By Bruno Rucy Carneiro Alves de Lima, Merlin Kramer, Kalmer Apinis
arXiv:2608. 10120v1 Announce Type: new Abstract: Modern sequence models, from Transformers to State Space Models, have enabled powerful generative modeling across diverse domains, yet they are typically trained to predict what happens while treating when it happens as a secondary concern.
By Adrien Schoen, Nachiketa Ratnakar Patil, Arjun Bhagoji, Francesco Bronzino