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One fundamental question in database theory is the following: Given a Boolean conjunctive queryQ, what is the best complexity for computing the answer to Q in terms of the input database sizeN? When restricted to the class of combinatorial algorithms, it is known that the best known complexity for any queryQis captured by thesubmodular widthofQ. However, beyond combinatorial algorithms, certain queries are known to admit faster algorithms that often involve a clever combination of fast matrix multiplication and data partitioning. Nevertheless, there is no systematic way to derive and analyze the complexity of such algorithms for arbitrary queriesQ. In this work, we introduce a general framework that captures the best complexity for answering any Boolean conjunctive queryQusing matrix multiplication. Our framework unifies both combinatorial and non-combinatorial techniques under the umbrella of information theory. It generalizes the notion of submodular width to a new stronger notion called the ω-submodular widththat naturally incorporates the power of fast matrix multiplication. We describe a matching algorithm that computes the answer to any queryQin time corresponding to the ω-submodularwidth ofQ. We show that our framework recovers the best known complexities for Boolean queries that have been studied in the literature, to the best of our knowledge, and also discovers new algorithms for some classes of queries that improve upon the best known complexities.
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This content will become publicly available on June 9, 2026
Smallest Synthetic Witnesses for Conjunctive Queries
Given a self-join-free conjunctive queryQand a set of tuplesS, asynthetic witness Dis a database instance such that the result ofQonDisS. In this work, we are interested in two problems. First, the existence problem ESW decides whether any synthetic witnessDexists. Second, given that a synthetic witness exists, the minimization problem SSW computes a synthetic witness of minimal size. The SSW problem is related to thesmallest witness problemrecently studied by Hu and Sintos [22]; however, the objective and the results are inherently different. More specifically, we show that SSW is poly-time solvable for a wider range of queries. Interestingly, in some cases, SSW is related to optimization problems in other domains, such as therole miningproblem in data mining and theedge concentrationproblem in graph drawing. Solutions to ESW and SSW are of practical interest, e.g., fortest database generationfor applications accessing a database and fordata compressionby encoding a datasetSas a pair of a queryQand databaseD. We prove that ESW is in P, presenting a simple algorithm that, given anyS, decides whether a synthetic witness exists in polynomial time in the size ofS. Next, we focus on the SSW problem. We show an algorithm that computes a minimal synthetic witness in polynomial time with respect to the size ofSfor any queryQthat has thehead-dominationproperty. IfQdoes not have such a property, then SSW is generally hard. More specifically, we show that for the class ofpath queries(of any constant length), SSW cannot be solved in polynomial time unless P = NP. We then extend this hardness result to the class ofBerge-acyclicqueries that do not have the head-domination property, obtaining a full dichotomy of SSW for Berge-acyclic queries. Finally, we investigate the hardness of SSW beyond Berge-acyclic queries by showing that SSW cannot be solved in polynomial time for some cyclic queries unless P = NP.
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- PAR ID:
- 10618185
- Publisher / Repository:
- PODS 2025
- Date Published:
- Journal Name:
- Proceedings of the ACM on Management of Data
- Volume:
- 3
- Issue:
- 2
- ISSN:
- 2836-6573
- Page Range / eLocation ID:
- 1 to 25
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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