Banzhaf Values for Facts in Query Answering
Omer Abramovich, Daniel Deutch, Nave Frost, Ahmet Kara, Dan Olteanu
摘要
Quantifying the contribution of database facts to query answers has been studied as means of explanation. The Banzhaf value, originally developed in Game Theory, is a natural measure of fact contribution, yet its efficient computation for select-project-join-union queries is challenging. In this paper, we introduce three algorithms to compute the Banzhaf value of database facts: an exact algorithm, an anytime deterministic approximation algorithm with relative error guarantees, and an algorithm for ranking and top-k. They have three key building blocks: compilation of query lineage into an equivalent function that allows efficient Banzhaf value computation; dynamic programming computation of the Banzhaf values of variables in a Boolean function using the Banzhaf values for constituent functions; and a mechanism to compute efficiently lower and upper bounds on Banzhaf values for any positive DNF function.
We complement the algorithms with a dichotomy for the Banzhaf-based ranking problem: given two facts, deciding whether the Banzhaf value of one is greater than of the other is tractable for hierarchical queries and intractable for non-hierarchical queries.
We show experimentally that our algorithms significantly outperform exact and approximate algorithms from prior work, most times up to two orders of magnitude. Our algorithms can also cover challenging problem instances that are beyond reach for prior work.
- Exact Banzhaf Computation. We introduce ExaBan, an algorithm that computes the exact Banzhaf scores for the contributions of facts in the answers to positive relational queries (Select-Project-Join-Union
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- SHAP Meets Tensor Networks: Provably Tractable Explanations with ParallelismReda Marzouk, Shahaf Bassan, Guy KatzNeurIPS 2025 · 被引用 9 次
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- From Decision Trees to Boolean Logic: A Fast and Unified SHAP AlgorithmAlexander Nadel, Ron WettensteinAAAI 2026 · 被引用 1 次
- Query-Guided Analysis and Mitigation of Data Verification ErrorsRan Schreiber, Yael AmsterdamerICDE 2026
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