Policy-Aware Federated Query Orchestration Across Energy Data Spaces and Edge AI Services
Summary: Demonstrates governance-aware federated orchestration for smart-grid analytics across energy data spaces and edge AI services. Combines LLM intent-to-workflow translation, policy-enforcing connectors, context-aware planning, and adaptive re-optimization under volatile resource and governance conditions. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Vasile Ofrim (Technical University of Cluj-Napoca)
- 2. Mihai Daian (Technical University of Cluj-Napoca)
- 3. Dragos Lazea (Technical University of Cluj-Napoca)
- 4. Liana Toderean (Technical University of Cluj-Napoca)
- 5. Anca Hangan (Technical University of Cluj-Napoca)
- 6. Tudor Cioara (Technical University of Cluj-Napoca)
BibTeX Citation
@article{ofrim_vldb26,
title = {{Policy-Aware Federated Query Orchestration Across Energy Data Spaces and Edge AI Services}},
author = {Ofrim, Vasile and Daian, Mihai and Lazea, Dragos and Toderean, Liana and Hangan, Anca and Cioara, Tudor},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4654--4657},
doi = {10.14778/3827998.3828089},
url = {https://doi.org/10.14778/3827998.3828089},
year = {2026}
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| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,142 | AOP: Automated and Interactive LLM Pipeline Orchestration for Answering Complex Queries | 2025 | CIDR | 6.7814795e-05 |
| 4,973 | Data Station: Delegated, Trustworthy, and Auditable Computation to Enable Data-Sharing Consortia with a Data Escrow | 2022 | VLDB | 6.3318963e-05 |
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