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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2510.04249 |
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| _version_ | 1866908577266925568 |
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| author | Lin, Yu-Ting Wang, Hsin-Po |
| author_facet | Lin, Yu-Ting Wang, Hsin-Po |
| contents | In a large database system, upper-bounding the cardinality of a join query is a crucial task called $\textit{pessimistic cardinality estimation}$. Recently, Abo Khamis, Nakos, Olteanu, and Suciu unified related works into the following dexterous framework. Step 1: Let $(X_1, \dotsc, X_n)$ be a random row of the join, equating $H(X_1, \dotsc, X_n)$ to the log of the join cardinality. Step 2: Upper-bound $H(X_1, \dotsc, X_n)$ using Shannon-type inequalities such as $H(X, Y, Z) \le H(X) + H(Y|X) + H(Z|Y)$. Step 3: Upper-bound $H(X_i) + p H(X_j | X_i)$ using the $p$-norm of the degree sequence of the underlying graph of a relation.
While old bound in step 3 count "claws $\in$" in the underlying graph, we proposed $\textit{ambidextrous}$ bounds that count "claw pairs ${\ni}\!{-}\!{\in}$". The new bounds are provably not looser and empirically tighter: they overestimate by $x^{3/4}$ times when the old bounds overestimate by $x$ times. An example is counting friend triples in the $\texttt{com-Youtube}$ dataset, the best dexterous bound is $1.2 \cdot 10^9$, the best ambidextrous bound is $5.1 \cdot 10^8$, and the actual cardinality is $1.8 \cdot 10^7$. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_04249 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Ambidextrous Degree Sequence Bounds for Pessimistic Cardinality Estimation Lin, Yu-Ting Wang, Hsin-Po Databases Information Theory In a large database system, upper-bounding the cardinality of a join query is a crucial task called $\textit{pessimistic cardinality estimation}$. Recently, Abo Khamis, Nakos, Olteanu, and Suciu unified related works into the following dexterous framework. Step 1: Let $(X_1, \dotsc, X_n)$ be a random row of the join, equating $H(X_1, \dotsc, X_n)$ to the log of the join cardinality. Step 2: Upper-bound $H(X_1, \dotsc, X_n)$ using Shannon-type inequalities such as $H(X, Y, Z) \le H(X) + H(Y|X) + H(Z|Y)$. Step 3: Upper-bound $H(X_i) + p H(X_j | X_i)$ using the $p$-norm of the degree sequence of the underlying graph of a relation. While old bound in step 3 count "claws $\in$" in the underlying graph, we proposed $\textit{ambidextrous}$ bounds that count "claw pairs ${\ni}\!{-}\!{\in}$". The new bounds are provably not looser and empirically tighter: they overestimate by $x^{3/4}$ times when the old bounds overestimate by $x$ times. An example is counting friend triples in the $\texttt{com-Youtube}$ dataset, the best dexterous bound is $1.2 \cdot 10^9$, the best ambidextrous bound is $5.1 \cdot 10^8$, and the actual cardinality is $1.8 \cdot 10^7$. |
| title | Ambidextrous Degree Sequence Bounds for Pessimistic Cardinality Estimation |
| topic | Databases Information Theory |
| url | https://arxiv.org/abs/2510.04249 |