Ensemble Masked Autoencoder and Gaussian Mixture Models for Cardinality Estimation
In recent years, deep learning models have been employed for cardinality estimation in databases. These models can be categorized into query-driven and data-driven approaches. Query-driven methods rely on training queries and lack generalization ability, while data-driven methods infer cardinalities...
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| Published in: | 2023 5th International Conference on Data-driven Optimization of Complex Systems (DOCS) pp. 1 - 7 |
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| Main Authors: | , , , |
| Format: | Conference Proceeding |
| Language: | English |
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IEEE
22.09.2023
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| Abstract | In recent years, deep learning models have been employed for cardinality estimation in databases. These models can be categorized into query-driven and data-driven approaches. Query-driven methods rely on training queries and lack generalization ability, while data-driven methods infer cardinalities by learning the joint distribution of data in the database, independent of specific queries. Some data-driven methods utilize deep autoregressive models, but they suffer from fixed attribute order during training and the issue of requiring large embedding matrices for continuous attributes. In order to overcome these challenges, we propose MAGMM, which combines masked autoencoder and Gaussian mixture models and is data-driven. Experimental results demonstrate that MAGMM performs well in terms of estimation accuracy and inference time, providing a robust solution for cardinality estimation in databases. |
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| AbstractList | In recent years, deep learning models have been employed for cardinality estimation in databases. These models can be categorized into query-driven and data-driven approaches. Query-driven methods rely on training queries and lack generalization ability, while data-driven methods infer cardinalities by learning the joint distribution of data in the database, independent of specific queries. Some data-driven methods utilize deep autoregressive models, but they suffer from fixed attribute order during training and the issue of requiring large embedding matrices for continuous attributes. In order to overcome these challenges, we propose MAGMM, which combines masked autoencoder and Gaussian mixture models and is data-driven. Experimental results demonstrate that MAGMM performs well in terms of estimation accuracy and inference time, providing a robust solution for cardinality estimation in databases. |
| Author | Li, Bo Lei, Ying Jia, Devin Chen, Yimeng |
| Author_xml | – sequence: 1 givenname: Yimeng surname: Chen fullname: Chen, Yimeng email: 202132803176@stu.hebut.edu.cn organization: School of Artificial Intelligence, Hebei University of Technology,Tianjin,China – sequence: 2 givenname: Devin surname: Jia fullname: Jia, Devin email: jiadx@inspur.com organization: Shandong Inspur Database Technology Co., Ltd.,Tianjin,China – sequence: 3 givenname: Ying surname: Lei fullname: Lei, Ying email: leiying.hebut@gmail.com organization: School of Artificial Intelligence, Hebei University of Technology,Tianjin,China – sequence: 4 givenname: Bo surname: Li fullname: Li, Bo email: 394078839@qq.com organization: Tianjin Jizhong Technology Co., Ltd.,Tianjin,China |
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| Snippet | In recent years, deep learning models have been employed for cardinality estimation in databases. These models can be categorized into query-driven and... |
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| SubjectTerms | cardinality estimation Complex systems data-driven Deep learning Estimation Gaussian mixture model masked autoencoder Mixture models Training |
| Title | Ensemble Masked Autoencoder and Gaussian Mixture Models for Cardinality Estimation |
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