Document Open Access Logo

Query Decompositions and All That (Invited Talk)

Authors Kyle Deeds , Timo Camillo Merkl , Reinhard Pichler , Dan Suciu

PDF

File

Thumbnail PDF
PDF
LIPIcs.ICDT.2026.1.pdf
  • Filesize: 0.9 MB
  • 20 pages

Document Identifiers

Subject Classification

ACM Subject Classification
  • Theory of computation → Database theory
  • Information systems → Query languages
Keywords
  • Query evaluation
  • Query decompositions
  • Complexity

Metrics

Abstract

The close relationship between Conjunctive Queries (CQs) and Constraint Satisfaction Problems (CSPs) has long been known. Nevertheless, apart from decomposition methods, research on efficient query evaluation or constraint solving algorithms has developed rather independently. In this article, we illustrate how search algorithms originating from the CSP community can be fruitfully applied to query evaluation - either by further developing the original search algorithms or by combining them with query decomposition methods. It turns out that the resulting approaches may indeed lead to lower time and/or space complexity than previous query evaluation methods.

Cite As Get BibTex

Kyle Deeds, Timo Camillo Merkl, Reinhard Pichler, and Dan Suciu. Query Decompositions and All That (Invited Talk). In 29th International Conference on Database Theory (ICDT 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 365, pp. 1:1-1:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ICDT.2026.1

Author Details

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

Funding

The work of Merkl and Pichler was supported by the Vienna Science and Technology Fund (WWTF) [10.47379/ICT2201, 10.47379/VRG18013, 10.47379/NXT22018]. Deeds and Suciu were partially supported by NSF IIS 2314527, NSF SHF 2312195, and NSF IIS 2507117.

References

  1. Christopher R. Aberger, Andrew Lamb, Susan Tu, Andres Nötzli, Kunle Olukotun, and Christopher Ré. Emptyheaded: A relational engine for graph processing. ACM Trans. Database Syst., 42(4):20:1-20:44, 2017. URL: https://doi.org/10.1145/3129246.
  2. Isolde Adler, Georg Gottlob, and Martin Grohe. Hypertree width and related hypergraph invariants. Eur. J. Comb., 28(8):2167-2181, 2007. URL: https://doi.org/10.1016/j.ejc.2007.04.013.
  3. Albert Atserias, Martin Grohe, and Dániel Marx. Size bounds and query plans for relational joins. In 49th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2008, October 25-28, 2008, Philadelphia, PA, USA, pages 739-748. IEEE Computer Society, 2008. URL: https://doi.org/10.1109/FOCS.2008.43.
  4. Albert Atserias, Martin Grohe, and Dániel Marx. Size bounds and query plans for relational joins. SIAM J. Comput., 42(4):1737-1767, 2013. URL: https://doi.org/10.1137/110859440.
  5. Paul Beame. A general sequential time-space tradeoff for finding unique elements. SIAM J. Comput., 20(2):270-277, 1991. URL: https://doi.org/10.1137/0220017.
  6. Liese Bekkers, Frank Neven, Stijn Vansummeren, and Yisu Remy Wang. Instance-optimal acyclic join processing without regret: Engineering the yannakakis algorithm in column stores. Proc. VLDB Endow., 18(8):2413-2426, 2025. URL: https://doi.org/10.14778/3742728.3742737.
  7. Altan Birler, Alfons Kemper, and Thomas Neumann. Robust join processing with diamond hardened joins. Proc. VLDB Endow., 17(11):3215-3228, 2024. URL: https://doi.org/10.14778/3681954.3681995.
  8. Angela Bonifati, Wim Martens, and Thomas Timm. An analytical study of large SPARQL query logs. VLDB J., 29(2-3):655-679, 2020. URL: https://doi.org/10.1007/s00778-019-00558-9.
  9. Chandra Chekuri and Anand Rajaraman. Conjunctive query containment revisited. Theor. Comput. Sci., 239(2):211-229, 2000. URL: https://doi.org/10.1016/S0304-3975(99)00220-0.
  10. Rina Dechter. Constraint Processing. Morgan Kaufmann Publishers, 2003. Google Scholar
  11. Rina Dechter. Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms, Second Edition. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2019. URL: https://doi.org/10.2200/S00893ED2V01Y201901AIM041.
  12. Kyle Deeds, Timo Camillo Merkl, Reinhard Pichler, and Dan Suciu. The space-time complexity of sum-product queries. CoRR, abs/2509.11920, 2025. Full version of a paper accepted by PODS 2026. URL: https://doi.org/10.48550/arXiv.2509.11920.
  13. Wolfgang Fischl, Georg Gottlob, Davide Mario Longo, and Reinhard Pichler. Hyperbench: A benchmark and tool for hypergraphs and empirical findings. ACM J. Exp. Algorithmics, 26:1.6:1-1.6:40, 2021. URL: https://doi.org/10.1145/3440015.
  14. Michael J. Freitag, Maximilian Bandle, Tobias Schmidt, Alfons Kemper, and Thomas Neumann. Adopting worst-case optimal joins in relational database systems. Proc. VLDB Endow., 13(11):1891-1904, 2020. URL: http://www.vldb.org/pvldb/vol13/p1891-freitag.pdf.
  15. Eugene C. Freuder and Michael J. Quinn. Taking advantage of stable sets of variables in constraint satisfaction problems. In Aravind K. Joshi, editor, Proceedings of the 9th International Joint Conference on Artificial Intelligence. Los Angeles, CA, USA, August 1985, pages 1076-1078. Morgan Kaufmann, 1985. URL: http://ijcai.org/Proceedings/85-2/Papers/082.pdf.
  16. Georg Gottlob, Matthias Lanzinger, Reinhard Pichler, and Igor Razgon. Complexity analysis of generalized and fractional hypertree decompositions. J. ACM, 68(5):38:1-38:50, 2021. URL: https://doi.org/10.1145/3457374.
  17. Georg Gottlob, Nicola Leone, and Francesco Scarcello. Hypertree decompositions and tractable queries. J. Comput. Syst. Sci., 64(3):579-627, 2002. URL: https://doi.org/10.1006/jcss.2001.1809.
  18. Georg Gottlob, Zoltán Miklós, and Thomas Schwentick. Generalized hypertree decompositions: NP-hardness and tractable variants. J. ACM, 56(6):30:1-30:32, 2009. URL: https://doi.org/10.1145/1568318.1568320.
  19. Johnnie Gray and Stefanos Kourtis. Hyper-optimized tensor network contraction. Quantum, 5:410, 2021. URL: https://doi.org/10.22331/Q-2021-03-15-410.
  20. Martin Grohe and Dániel Marx. Constraint solving via fractional edge covers. ACM Trans. Algorithms, 11(1):4:1-4:20, 2014. URL: https://doi.org/10.1145/2636918.
  21. Sunwoong Joo, Attila Dusnoki, Martyn Bliss, Ben Duckworth, Nicolas Scotto Di Perto, Markó Fabó, Gábor Lóki, Dániel Vince, Ákos Kiss, and Cheul-hee Hahm. A memory-aware performance optimization of tensor programs for embedded devices. In 2020 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pages 1-4. IEEE, 2020. Google Scholar
  22. Manolis Katsaragakis, Lazaros Papadopoulos, Mario Konijnenburg, Francky Catthoor, and Dimitrios Soudris. Memory footprint optimization techniques for machine learning applications in embedded systems. In IEEE International Symposium on Circuits and Systems, ISCAS 2020, Sevilla, Spain, October 10-21, 2020, pages 1-4. IEEE, 2020. URL: https://doi.org/10.1109/ISCAS45731.2020.9181038.
  23. Mahmoud Abo Khamis, Hung Q. Ngo, and Atri Rudra. FAQ: questions asked frequently. CoRR, abs/1504.04044, 2015. URL: https://arxiv.org/abs/1504.04044.
  24. Phokion G. Kolaitis and Moshe Y. Vardi. Conjunctive-query containment and constraint satisfaction. J. Comput. Syst. Sci., 61(2):302-332, 2000. URL: https://doi.org/10.1006/JCSS.2000.1713.
  25. Matthias Lanzinger, Cem Okulmus, Reinhard Pichler, Alexander Selzer, and Georg Gottlob. Soft and constrained hypertree width. Proc. ACM Manag. Data, 3(2):114:1-114:25, 2025. URL: https://doi.org/10.1145/3725251.
  26. Matthias Lanzinger, Reinhard Pichler, and Alexander Selzer. Avoiding materialisation for guarded aggregate queries. Proc. VLDB Endow., 18(5):1398-1411, 2025. URL: https://www.vldb.org/pvldb/vol18/p1398-selzer.pdf.
  27. Stanislav Malyshev, Markus Krötzsch, Larry González, Julius Gonsior, and Adrian Bielefeldt. Getting the most out of wikidata: Semantic technology usage in wikipedia’s knowledge graph. In Denny Vrandecic, Kalina Bontcheva, Mari Carmen Suárez-Figueroa, Valentina Presutti, Irene Celino, Marta Sabou, Lucie-Aimée Kaffee, and Elena Simperl, editors, The Semantic Web - ISWC 2018 - 17th International Semantic Web Conference, Monterey, CA, USA, October 8-12, 2018, Proceedings, Part II, volume 11137 of Lecture Notes in Computer Science, pages 376-394. Springer, 2018. URL: https://doi.org/10.1007/978-3-030-00668-6_23.
  28. Dylan M. McKay and Richard Ryan Williams. Quadratic time-space lower bounds for computing natural functions with a random oracle. In Avrim Blum, editor, 10th Innovations in Theoretical Computer Science Conference, ITCS 2019, January 10-12, 2019, San Diego, California, USA, volume 124 of LIPIcs, pages 56:1-56:20. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, 2019. URL: https://doi.org/10.4230/LIPIcs.ITCS.2019.56.
  29. Hung Q. Ngo, Christopher Ré, and Atri Rudra. Skew strikes back: new developments in the theory of join algorithms. SIGMOD Rec., 42(4):5-16, 2013. URL: https://doi.org/10.1145/2590989.2590991.
  30. Samyam Rajbhandari, Olatunji Ruwase, Jeff Rasley, Shaden Smith, and Yuxiong He. Zero-infinity: breaking the GPU memory wall for extreme scale deep learning. In Bronis R. de Supinski, Mary W. Hall, and Todd Gamblin, editors, International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2021, St. Louis, Missouri, USA, November 14-19, 2021, page 59. ACM, 2021. URL: https://doi.org/10.1145/3458817.3476205.
  31. Neil Robertson and Paul D. Seymour. Graph minors. II. algorithmic aspects of tree-width. J. Algorithms, 7(3):309-322, 1986. URL: https://doi.org/10.1016/0196-6774(86)90023-4.
  32. Francesco Scarcello, Gianluigi Greco, and Nicola Leone. Weighted hypertree decompositions and optimal query plans. J. Comput. Syst. Sci., 73(3):475-506, 2007. URL: https://doi.org/10.1016/J.JCSS.2006.10.010.
  33. Kian-Lee Tan, Qingchao Cai, Beng Chin Ooi, Weng-Fai Wong, Chang Yao, and Hao Zhang. In-memory databases: Challenges and opportunities from software and hardware perspectives. SIGMOD Rec., 44(2):35-40, 2015. URL: https://doi.org/10.1145/2814710.2814717.
  34. Todd L. Veldhuizen. Triejoin: A simple, worst-case optimal join algorithm. In Nicole Schweikardt, Vassilis Christophides, and Vincent Leroy, editors, Proc. 17th International Conference on Database Theory (ICDT), Athens, Greece, March 24-28, 2014, pages 96-106. OpenProceedings.org, 2014. URL: https://doi.org/10.5441/002/ICDT.2014.13.
  35. Qichen Wang, Bingnan Chen, Binyang Dai, Ke Yi, Feifei Li, and Liang Lin. Yannakakis+: Practical acyclic query evaluation with theoretical guarantees. Proc. ACM Manag. Data, 3(3):235:1-235:28, 2025. URL: https://doi.org/10.1145/3725423.
  36. Mihalis Yannakakis. Algorithms for acyclic database schemes. In Proceedings VLDB, pages 82-94. VLDB, 1981. Google Scholar
Any Issues?
X

Feedback on the Current Page

CAPTCHA

Thanks for your feedback!

Feedback submitted to Dagstuhl Publishing

Could not send message

Please try again later or send an E-mail
© 2023-2025 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact