AOP: Automated and Interactive LLM Pipeline Orchestration for Answering Complex Queries
Summary: AOP: first system for automated, interactive LLM pipeline orchestration on data lakes; defines reusable semantic operators (retrieval, filter, aggregate, validate) to assemble multi‑step workflows across unstructured, semi‑structured, and structured data. LLM‑driven operator extraction plus pipeline optimizations (prefetching, parallelism, interactive self‑reflection) yields large accuracy gains (≈45%) on complex query benchmarks. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiayi Wang (Tsinghua University)
- 2. Guoliang Li (Tsinghua University)
BibTeX Citation
@inproceedings{wang_cidr25,
address = {Amsterdam, Netherlands},
series = {{CIDR} '25},
title = {{AOP: Automated and Interactive LLM Pipeline Orchestration for Answering Complex Queries}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Wang, Jiayi and Li, Guoliang},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 501 | Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes | 2024 | VLDB | 0.00017267905 |
| 669 | CAESURA: Language Models as Multi-Modal Query Planners | 2024 | CIDR | 0.0001495987 |
| 1,392 | Symphony: Towards Natural Language Query Answering over Multi-modal Data Lakes | 2023 | CIDR | 0.00010807936 |
| 1,658 | D-Bot: Database Diagnosis System using Large Language Models | 2024 | VLDB | 9.9642078e-05 |
| 2,348 | The Dawn of Natural Language to SQL: Are We Fully Ready? | 2024 | VLDB | 8.6009821e-05 |
| 3,741 | FACE: A Normalizing Flow based Cardinality Estimator | 2022 | VLDB | 7.0594076e-05 |
| 4,557 | Databases Unbound: Querying All of the World’s Bytes with AI | 2024 | VLDB | 6.5356905e-05 |
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