Software Technology Research
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App. Res. of Comp. Printed Article
Vol. 43, 2026 No. 4
1171-1179

Database cardinality estimation method based on adaptive Gaussian mixture model

Li Hao
Liu Mengchi
Zou Ruiji
Liu Mingkai
School of Computer Science, South China Normal University, Guangzhou 510000, China
DOI: 10.19734/j.issn.1001-3695.2025.07.0292

Abstract

Cardinality estimation is a critical component of database query optimization, where its accuracy directly impacts the execution efficiency of query plans. Deep autoregressive model-based cardinality estimators have demonstrated remarkable accuracy in prior studies. However, they struggle to capture data distribution patterns when handling large-domain continuous attributes, which lead to significant performance degradation. To address these challenges, this paper proposed a novel cardinality estimator based on an adaptive Gaussian mixture model, called AGCard. It first dynamically adjusted the number and parameters of Gaussian components to adaptively fit the data distribution of continuous attributes, thereby reducing the domain scale. Subsequently, AGCard employed a bias correction algorithm to compensate for the estimation deviations introduced by the progressive sampling process while avoiding additional computational overhead. Extensive experiments on three real-world datasets(including WISDM) demonstrate that the proposed method outperforms existing mainstream baselines in terms of estimation accuracy, inference latency, and storage overhead. The results confirm the effectiveness of the adaptive Gaussian mixture model and the bias correction algorithm.

Foundation Support

国家自然科学基金资助项目(61672389)
广州市大数据智能教育重点实验室(201905010009)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.07.0292
Publish at: Application Research of Computers Printed Article, Vol. 43, 2026 No. 4
Section: Software Technology Research
Pages: 1171-1179
Serial Number: 1001-3695(2026)04-024-1171-09

Publish History

[2026-04-05] Printed Article

Cite This Article

李昊, 刘梦赤, 邹瑞基, 等. 基于自适应高斯混合模型的数据库基数估计方法 [J]. 计算机应用研究, 2026, 43 (4): 1171-1179. (Li Hao, Liu Mengchi, Zou Ruiji, et al. Database cardinality estimation method based on adaptive Gaussian mixture model [J]. Application Research of Computers, 2026, 43 (4): 1171-1179. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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