---
title: "VLDB (A*): papers and universities"
description: "VLDB (International Conference on Very Large Databases) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,274 of its papers from 2024–2026 (447 in 2024, 442 in 2025, 385 in 2026). By the countries of the authors' institutions, the most papers come from China (551), the United States (457) and Hong Kong (124)."
url: https://scholar4ai.com/proceedings/vldb
markdown_url: https://scholar4ai.com/proceedings/vldb.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "VLDB (International Conference on Very Large Databases) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,274 of its papers from 2024–2026 (447 in 2024, 442 in 2025, 385 in 2026). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# VLDB: International Conference on Very Large Databases — papers and institutions, 2024–2026

Papers of VLDB, International Conference on Very Large Databases (CORE rank A*; field: Data management and data science), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02.

All conferences: https://scholar4ai.com/proceedings.md

## Key findings

- VLDB (International Conference on Very Large Databases) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,274 of its papers from 2024–2026 (447 in 2024, 442 in 2025, 385 in 2026).
- By the countries of the authors' institutions, the most papers come from China (551), the United States (457) and Hong Kong (124).
- The institutions with the most papers are Tsinghua University (76), The Hong Kong University of Science and Technology (Guangzhou) (65) and Zhejiang University (64).
- Its most-cited paper of these years is “Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation” (2024), with 343 citations.

## Papers per year

*VLDB papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 447 | 443 |
| 2025 | 442 | 438 |
| 2026 | 385 | 373 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4210226185](https://openalex.org/S4210226185) | Conference-linked journal |

## Who publishes there

*Institutions with the most VLDB papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 76 |
| 2 | The Hong Kong University of Science and Technology (Guangzhou) | 65 |
| 3 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 64 |
| 4 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 59 |
| 5 | Hong Kong University of Science and Technology | 55 |
| 6 | Microsoft (United States) | 44 |
| 7 | Alibaba Group (China) | 44 |
| 8 | [Renmin University of China](https://scholar4ai.com/institutions/renmin-university-of-china.md) | 41 |
| 9 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 39 |
| 10 | [Beijing Institute of Technology](https://scholar4ai.com/institutions/beijing-institute-of-technology.md) | 37 |

*Countries with the most VLDB papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 551 |
| 2 | United States | 457 |
| 3 | Hong Kong | 124 |
| 4 | Germany | 122 |
| 5 | Singapore | 71 |
| 6 | Canada | 67 |
| 7 | Australia | 57 |
| 8 | Denmark | 46 |
| 9 | France | 45 |
| 10 | United Kingdom | 37 |

*Keywords with the most VLDB papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 136 |
| 2 | query optimization | 70 |
| 3 | scalability | 49 |
| 4 | large-scale graphs | 37 |
| 5 | [graph neural networks](https://scholar4ai.com/keywords/graph-neural-networks.md) | 36 |
| 6 | approximate nearest neighbor search | 35 |
| 7 | TPC-H | 33 |
| 8 | dynamic graphs | 29 |
| 9 | data cleaning | 28 |
| 10 | data lakes | 27 |

## Most-cited papers

*Most-cited VLDB papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 343 | [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://doi.org/10.14778/3641204.3641221) | 2024-01-01 | Dawei Gao, Haibin Wang, Yaliang Li et al. |
| 2 | 167 | [TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods](https://doi.org/10.14778/3665844.3665863) | 2024-05-01 | Xiangfei Qiu, Jilin Hu, Lekui Zhou et al. |
| 3 | 89 | [BigST: Linear Complexity Spatio-Temporal Graph Neural Network for Traffic Forecasting on Large-Scale Road Networks](https://doi.org/10.14778/3641204.3641217) | 2024-01-01 | Jindong Han, Weijia Zhang, Hao Liu et al. |
| 4 | 82 | [The Dawn of Natural Language to SQL: Are We Fully Ready?](https://doi.org/10.14778/3681954.3682003) | 2024-07-01 | Boyan Li, Yuyu Luo, Chengliang Chai et al. |
| 5 | 59 | [D-Bot: Database Diagnosis System using Large Language Models](https://doi.org/10.14778/3675034.3675043) | 2024-06-01 | Xuanhe Zhou, Guoliang Li, Zhaoyan Sun et al. |
| 6 | 58 | [GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization](https://doi.org/10.14778/3659437.3659449) | 2024-04-01 | Jiale Lao, Yibo Wang, Yufei Li et al. |
| 7 | 58 | [A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis](https://doi.org/10.14778/3654621.3654637) | 2024-03-01 | Shuhan Zhong, Sizhe Song, Weipeng Zhuo et al. |
| 8 | 50 | [ClickHouse - Lightning Fast Analytics for Everyone](https://doi.org/10.14778/3685800.3685802) | 2024-08-01 | Robert Schulze, Tom Schreiber, Ilya Yatsishin et al. |
| 9 | 50 | [Combining Small Language Models and Large Language Models for Zero-Shot NL2SQL](https://doi.org/10.14778/3681954.3681960) | 2024-07-01 | Ju Fan, Zihui Gu, Songyue Zhang et al. |
| 10 | 50 | [ReAcTable: Enhancing ReAct for Table Question Answering](https://doi.org/10.14778/3659437.3659452) | 2024-04-01 | Yunjia Zhang, Jordan Henkel, Avrilia Floratou et al. |

## Cite this page

Scholar4AI. (2026). VLDB: International Conference on Very Large Databases — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/vldb

```bibtex
@misc{scholar4ai-proceedings-vldb,
  author = {{Scholar4AI}},
  title = {VLDB: International Conference on Very Large Databases — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/vldb}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).