SIGMOD: ACM Special Interest Group on Management of Data Conference — papers and institutions, 2024–2026

Papers of SIGMOD, ACM Special Interest Group on Management of Data Conference (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#

SIGMOD (ACM Special Interest Group on Management of Data Conference) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 814 of its papers from 2024–2026 (212 in 2024, 333 in 2025, 269 in 2026).
By the countries of the authors' institutions, the most papers come from China (383), the United States (242) and Singapore (96).
The institutions with the most papers are Tsinghua University (64), Nanyang Technological University (49) and National University of Singapore (45).
Its most-cited paper of these years is “CodeS: Towards Building Open-source Language Models for Text-to-SQL” (2024), with 134 citations.

Papers per year#

SIGMOD papers per publication year in OpenAlex (–: not in OpenAlex yet)
Year	Papers	With affiliations
2024	212	212
2025	333	333
2026	269	269

Sources#

Where OpenAlex lists the papers
OpenAlex source	Which papers
S4387289859	Conference-linked journal (issue 1, 3, 4, 5, 6)

Who publishes there#

Institutions with the most SIGMOD papers, 2024–2026
#	Institution	Papers
1	Tsinghua University	64
2	Nanyang Technological University	49
3	National University of Singapore	45
4	Zhejiang University	42
5	Shanghai Jiao Tong University	40
6	Hong Kong University of Science and Technology	35
7	Alibaba Group (China)	32
8	Chinese University of Hong Kong	31
9	Beijing Institute of Technology	27
10	Renmin University of China	27

Countries with the most SIGMOD papers, 2024–2026
#	Country	Papers
1	China	383
2	United States	242
3	Singapore	96
4	Hong Kong	91
5	Germany	56
6	Australia	50
7	Canada	35
8	United Kingdom	29
9	Switzerland	25
10	France	23

Keywords with the most SIGMOD papers, 2024–2026
#	Keyword	Papers
1	large language models	75
2	query optimization	48
3	approximate nearest neighbor search	43
4	approximation algorithms	26
5	database management systems	26
6	scalability	24
7	LSM-tree	23
8	large-scale graphs	19
9	RocksDB	18
10	cardinality estimation	18

Most-cited papers#

Most-cited SIGMOD papers of 2024–2026 (citations counted by OpenAlex)
#	Citations	Title	Published	Authors
1	134	CodeS: Towards Building Open-source Language Models for Text-to-SQL	2024-05-29	Haoyang Li, Jing Zhang, Hanbing Liu et al.
2	88	RaBitQ: Quantizing High-Dimensional Vectors with a Theoretical Error Bound for Approximate Nearest Neighbor Search	2024-05-29	Jianyang Gao, Cheng Long
3	66	Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks	2024-05-29	Peng Li, Yeye He, Dror Yashar et al.
4	64	Starling: An I/O-Efficient Disk-Resident Graph Index Framework for High-Dimensional Vector Similarity Search on Data Segment	2024-03-12	Mengzhao Wang, Weizhi Xu, Xiaomeng Yi et al.
5	62	ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data	2024-05-29	Liana Patel, Peter Kraft, Carlos Guestrin et al.
6	47	Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study	2024-05-29	Yang Wu, Yao Wan, Hongyu Zhang et al.
7	43	SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search	2024-03-12	Chaoji Zuo, Miao Qiao, Wenchao Zhou et al.
8	38	Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art	2025-02-10	Ilias Azizi, Karima Echihabi, Themis Palpanas
9	36	A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction	2024-03-12	Zhijie Zhang, Yujie Lu, Weiguo Zheng et al.
10	33	Practical and Asymptotically Optimal Quantization of High-Dimensional Vectors in Euclidean Space for Approximate Nearest Neighbor Search	2025-06-17	Jianyang Gao, Yutong Gou, Yuexuan Xu et al.

Conferences in the same field#

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

Cite this page

Scholar4AI. (2026). SIGMOD: ACM Special Interest Group on Management of Data Conference — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/sigmod

@misc{scholar4ai-proceedings-sigmod,
  author = {{Scholar4AI}},
  title = {SIGMOD: ACM Special Interest Group on Management of Data Conference — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/sigmod}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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