ACE: A Cardinality Estimator for Set-Valued Queries
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866910883449405440 |
|---|---|
| author | Sheng, Yufan Cao, Xin Zhao, Kaiqi Fang, Yixiang Qi, Jianzhong Zhang, Wenjie Jensen, Christian S. |
| author_facet | Sheng, Yufan Cao, Xin Zhao, Kaiqi Fang, Yixiang Qi, Jianzhong Zhang, Wenjie Jensen, Christian S. |
| contents | Cardinality estimation is a fundamental functionality in database systems. Most existing cardinality estimators focus on handling predicates over numeric or categorical data. They have largely omitted an important data type, set-valued data, which frequently occur in contemporary applications such as information retrieval and recommender systems. The few existing estimators for such data either favor high-frequency elements or rely on a partial independence assumption, which limits their practical applicability. We propose ACE, an Attention-based Cardinality Estimator for estimating the cardinality of queries over set-valued data. We first design a distillation-based data encoder to condense the dataset into a compact matrix. We then design an attention-based query analyzer to capture correlations among query elements. To handle variable-sized queries, a pooling module is introduced, followed by a regression model (MLP) to generate final cardinality estimates. We evaluate ACE on three datasets with varying query element distributions, demonstrating that ACE outperforms the state-of-the-art competitors in terms of both accuracy and efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ACE: A Cardinality Estimator for Set-Valued Queries Sheng, Yufan Cao, Xin Zhao, Kaiqi Fang, Yixiang Qi, Jianzhong Zhang, Wenjie Jensen, Christian S. Databases Machine Learning Cardinality estimation is a fundamental functionality in database systems. Most existing cardinality estimators focus on handling predicates over numeric or categorical data. They have largely omitted an important data type, set-valued data, which frequently occur in contemporary applications such as information retrieval and recommender systems. The few existing estimators for such data either favor high-frequency elements or rely on a partial independence assumption, which limits their practical applicability. We propose ACE, an Attention-based Cardinality Estimator for estimating the cardinality of queries over set-valued data. We first design a distillation-based data encoder to condense the dataset into a compact matrix. We then design an attention-based query analyzer to capture correlations among query elements. To handle variable-sized queries, a pooling module is introduced, followed by a regression model (MLP) to generate final cardinality estimates. We evaluate ACE on three datasets with varying query element distributions, demonstrating that ACE outperforms the state-of-the-art competitors in terms of both accuracy and efficiency. |
| title | ACE: A Cardinality Estimator for Set-Valued Queries |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2503.14929 |