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Partition Constraints for Conjunctive Queries: Bounds and Worst-Case Optimal Joins

Authors Kyle Deeds , Timo Camillo Merkl

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Author Details

Kyle Deeds
  • University of Washington, Seattle, WA, USA
Timo Camillo Merkl
  • TU Wien, Austria

Funding

  • Deeds, Kyle: Was supported by the NSF SHF 2312195 and NSF IIS 2314527 grants.
  • Merkl, Timo Camillo: Was supported by the Vienna Science and Technology Fund (WWTF) [10.47379/ICT2201, 10.47379/VRG18013].

Acknowledgements

Part of this work was done when the authors were visiting the Simons Institute for the Theory of Computing.

Cite As Get BibTex

Kyle Deeds and Timo Camillo Merkl. Partition Constraints for Conjunctive Queries: Bounds and Worst-Case Optimal Joins. In 28th International Conference on Database Theory (ICDT 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 328, pp. 17:1-17:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/LIPIcs.ICDT.2025.17

Abstract

In the last decade, various works have used statistics on relations to improve both the theory and practice of conjunctive query execution. Starting with the AGM bound which took advantage of relation sizes, later works incorporated statistics like functional dependencies and degree constraints. Each new statistic prompted work along two lines; bounding the size of conjunctive query outputs and worst-case optimal join algorithms. In this work, we continue in this vein by introducing a new statistic called a partition constraint. This statistic captures latent structure within relations by partitioning them into sub-relations which each have much tighter degree constraints. We show that this approach can both refine existing cardinality bounds and improve existing worst-case optimal join algorithms.

Subject Classification

ACM Subject Classification
  • Theory of computation → Database theory
Keywords
  • Worst-Case Optimal Joins
  • Cardinality Bounds
  • Degeneracy
  • Degree Constraints
  • Partition Constraints

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