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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.18758410 |
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| _version_ | 1866902040590942208 |
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| author | Meo, Muhammad Umair |
| author_facet | Meo, Muhammad Umair |
| 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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18758410 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AutoData Steward: An AI-Driven Framework for FAIR-Compliant Data Lifecycle Optimization in HPC Environments (Conference Abstract) Meo, Muhammad Umair <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> |
| title | AutoData Steward: An AI-Driven Framework for FAIR-Compliant Data Lifecycle Optimization in HPC Environments (Conference Abstract) |
| url | https://doi.org/10.5281/zenodo.18758410 |