Hugging Face Trending Papers
Automated Event Log Generation from Unstructured Text Using Finetuned LLMs
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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.
arXiv AI
Sep 10
From Event Logs to Governed Action: A BlueSky Agenda for Agentic Process Mining
arXiv:2609.07984v1 Announce Type: new Abstract: Process mining has long turned event logs into process knowledge: discovered models, conformance evidence, bottleneck diagnoses, and runtime prediction...
arXiv Computation and Language
Aug 25
A Scalable Cross-Domain Event Extraction System via a Unified Generative Training Framework
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.
arXiv AI
Jun 15
Abstracting Cross-Domain Action Sequences into Interpretable Workflows
arXiv:2606. 14654v1 Announce Type: new Abstract: Sequential or time-stamped interaction logs provide objective records of digital application usage, yet their granularity and noise often obscure meaningful insights into people's work.
arXiv AI
Jun 3
Efficient Temporal Datalog Materialisation for Composite Event Recognition
arXiv:2605. 02488v2 Announce Type: replace Abstract: Several applications demand the timely detection of critical situations, such as threats to safety and transparency, over high-velocity streams of symbolic events.
arXiv AI
Sep 18
Semantic Layer Induction from Raw Telemetry via Hierarchical LLM and RAG Abstraction
The paper introduces an end‑to‑end framework that automatically builds a business semantic layer from raw application logs. It uses a two‑stage abstraction: first, large language models identify high‑level business features with industry knowledge, and second, a structured pipeline refines data, retrieves relevant information, filters, clusters semantically, and assigns canonical names. Evaluation on production‑scale telemetry shows significant gains in semantic quality, noise reduction, and maintenance effort, with a 0.87 Cohen’s kappa in an LLM‑as‑Judge assessment.