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. Also as Markdown.

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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	Conference-linked journal

Who publishes there#

Institutions with the most VLDB papers, 2024–2026
#	Institution	Papers
1	Tsinghua University	76
2	The Hong Kong University of Science and Technology (Guangzhou)	65
3	Zhejiang University	64
4	Shanghai Jiao Tong University	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	41
9	National University of Singapore	39
10	Beijing Institute of Technology	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	136
2	query optimization	70
3	scalability	49
4	large-scale graphs	37
5	graph neural networks	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	2024-01-01	Dawei Gao, Haibin Wang, Yaliang Li et al.
2	167	TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods	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	2024-01-01	Jindong Han, Weijia Zhang, Hao Liu et al.
4	82	The Dawn of Natural Language to SQL: Are We Fully Ready?	2024-07-01	Boyan Li, Yuyu Luo, Chengliang Chai et al.
5	59	D-Bot: Database Diagnosis System using Large Language Models	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	2024-04-01	Jiale Lao, Yibo Wang, Yufei Li et al.
7	58	A Multi-Scale Decomposition MLP-Mixer for Time Series Analysis	2024-03-01	Shuhan Zhong, Sizhe Song, Weipeng Zhuo et al.
8	50	ClickHouse - Lightning Fast Analytics for Everyone	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	2024-07-01	Ju Fan, Zihui Gu, Songyue Zhang et al.
10	50	ReAcTable: Enhancing ReAct for Table Question Answering	2024-04-01	Yunjia Zhang, Jordan Henkel, Avrilia Floratou et al.

Conferences in the same field#

KDD · WWW · SIGIR · ICDE · SIGMOD · ICDM · PODS · CIKM · ECML PKDD · RecSys · ECIR · WSDM · ICWSM · ICDAR · MSR · SIGSPATIAL · ISWC · SDM · CAiSE · ICDT · ER

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

@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}
}

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