---
title: "SIGMOD (A*): papers and universities"
description: "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)."
url: https://scholar4ai.com/proceedings/sigmod
markdown_url: https://scholar4ai.com/proceedings/sigmod.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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). (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)
---

# 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.

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

## 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](https://openalex.org/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](https://scholar4ai.com/institutions/tsinghua-university.md) | 64 |
| 2 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 49 |
| 3 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 45 |
| 4 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 42 |
| 5 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 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](https://scholar4ai.com/institutions/beijing-institute-of-technology.md) | 27 |
| 10 | [Renmin University of China](https://scholar4ai.com/institutions/renmin-university-of-china.md) | 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](https://scholar4ai.com/keywords/large-language-models.md) | 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](https://doi.org/10.1145/3654930) | 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](https://doi.org/10.1145/3654970) | 2024-05-29 | Jianyang Gao, Cheng Long |
| 3 | 66 | [Table-GPT: Table Fine-tuned GPT for Diverse Table Tasks](https://doi.org/10.1145/3654979) | 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](https://doi.org/10.1145/3639269) | 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](https://doi.org/10.1145/3654923) | 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](https://doi.org/10.1145/3654992) | 2024-05-29 | Yang Wu, Yao Wan, Hongyu Zhang et al. |
| 7 | 43 | [SeRF: Segment Graph for Range-Filtering Approximate Nearest Neighbor Search](https://doi.org/10.1145/3639324) | 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](https://doi.org/10.1145/3709693) | 2025-02-10 | Ilias Azizi, Karima Echihabi, Themis Palpanas |
| 9 | 36 | [A Comprehensive Survey and Experimental Study of Subgraph Matching: Trends, Unbiasedness, and Interaction](https://doi.org/10.1145/3639315) | 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](https://doi.org/10.1145/3725413) | 2025-06-17 | Jianyang Gao, Yutong Gou, Yuexuan Xu et al. |

## 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

```bibtex
@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}
}
```

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