NeurIDA: Dynamic Modeling for Effective In-Database Analytics

Summary: NeurIDA introduces dynamic in-database modeling: pretrained, composable base components are selected and configured per analytical task and data profile, replacing bespoke pipelines. Natural-language task formulation and LLM-generated reports enable autonomous, reusable relational analytics with improved accuracy and efficiency. (summarized by gpt-5.6-luna on Aug 17 2026)

Paper ID: h83b4ed9b594a1572
Venue: VLDB
Year: 2026
Pagerank: 4.9793485e-05
Overall Rank: 10,798 | 27.41%
DOI: 10.14778/3819518.3819563

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Lingze Zeng (National University of Singapore)
2. Shaofeng Cai (National University of Singapore)
3. Naili Xing (National University of Singapore)
4. Jiaqi Zhu (National University of Singapore)
5. Gang Chen (Zhejiang University)
6. Peng Lu (Zhejiang University)
7. Jian Pei (Duke University)
8. Beng Chin Ooi (Zhejiang University)

BibTeX Citation

@article{zeng_vldb26,
        title = {{NeurIDA: Dynamic Modeling for Effective In-Database Analytics}},
        author = {Zeng, Lingze and Cai, Shaofeng and Xing, Naili and Zhu, Jiaqi and Chen, Gang and Lu, Peng and Pei, Jian and Ooi, Beng Chin},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {9},
        pages = {2452--2465},
        doi = {10.14778/3819518.3819563},
        url = {https://doi.org/10.14778/3819518.3819563},
        year = {2026}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 23 of 23 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
1	Access Path Selection in a Relational Database Management System	1979	SIGMOD	0.0023947656
34	The Design Of Postgres	1986	SIGMOD	0.00049142315
78	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036684414
105	The MADlib Analytics Library or MAD Skills, the SQL	2012	VLDB	0.00033638251
272	An Overview of Query Optimization in Relational Systems	1998	PODS	0.00022509573
884	Dynamic Programming Strikes Back	2008	SIGMOD	0.00013267935
1,038	ARDA: Automatic Relational Data Augmentation for Machine Learning	2020	VLDB	0.00012370691
1,152	Cerebro: A Data System for Optimized Deep Learning Model Selection	2020	VLDB	0.00011801961
2,182	Extending Relational Query Processing with ML Inference	2020	CIDR	8.8982998e-05
2,662	End-to-end Optimization of Machine Learning Prediction Queries	2022	SIGMOD	8.1596229e-05
3,617	Leva: Boosting Machine Learning Performance with Relational Embedding Data Augmentation	2022	SIGMOD	7.1574349e-05
4,489	ARM-Net: Adaptive Relation Modeling Network for Structured Data	2021	SIGMOD	6.5781098e-05
5,015	Discovering Related Data At Scale	2021	VLDB	6.3135569e-05
5,401	RONIN: Data Lake Exploration	2021	VLDB	6.1462648e-05
5,716	Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data	2023	SIGMOD	6.0194657e-05
5,865	Modeling Shifting Workloads for Learned Database Systems	2024	SIGMOD	5.9659203e-05
8,385	NeurDB: On the Design and Implementation of an AI-powered Autonomous Database	2025	CIDR	5.3420959e-05
8,947	Machine Unlearning in Learned Databases: An Experimental Analysis	2024	SIGMOD	5.2532248e-05
9,158	Database Perspective on LLM Inference Systems	2025	VLDB	5.2164603e-05
9,212	Powering In-Database Dynamic Model Slicing for Structured Data Analytics	2024	VLDB	5.2060505e-05
9,996	Flow with FlorDB: Incremental Context Maintenance for the Machine Learning Lifecycle	2025	CIDR	5.0979044e-05
10,000	cedar: Optimized and Unified Machine Learning Input Data Pipelines	2025	VLDB	5.0979044e-05
11,549	Database Native Model Selection: Harnessing Deep Neural Networks in Database Systems	2024	VLDB	4.9793485e-05

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