Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
|
| Online Access: | https://doi.org/10.5281/zenodo.18758410 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- <p>This record contains the accepted conference abstract of the work titled:</p> <p>"AutoData Steward: An AI-Driven Framework for FAIR-Compliant Data Lifecycle Optimization in HPC Environments."</p> <p>The proposed framework introduces an AI-driven approach for intelligent data lifecycle management in High Performance Computing (HPC) environments. It integrates FAIR data principles (Findable, Accessible, Interoperable, Reusable) with automated metadata enrichment, machine learning-based storage optimization, and policy-driven lifecycle control mechanisms.</p> <p>The system aims to improve storage efficiency, enhance research data discoverability, and support sustainable data stewardship in large-scale computing infrastructures.</p> <p>This record includes the officially published one-page abstract as it appeared in the NHR Conference 2026 Book of Abstracts.</p> <p>Keywords: Artificial Intelligence, FAIR Data, High Performance Computing, Data Lifecycle Management, Research Data Management, Storage Optimization.</p>