Soft and Constrained Hypertree Width
Summary: Proposes soft hypertree width (shw), a relaxation of hypertree width for CQ evaluation via candidate decompositions. Shw never exceeds hw and can be smaller, preserving tractability while enabling constraints and preferences; prototype experiments show practical impact. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Matthias Lanzinger (Vienna University of Technology)
- 2. Cem Okulmus (Paderborn University)
- 3. Reinhard Pichler (Vienna University of Technology)
- 4. Alexander Selzer (Vienna University of Technology)
- 5. Georg Gottlob (University of Calabria)
BibTeX Citation
@inproceedings{lanzinger_pods25,
address = {New York, NY, USA},
series = {{PODS} '25},
title = {{Soft and Constrained Hypertree Width}},
url = {https://dl.acm.org/doi/10.1145/3725251},
doi = {10.1145/3725251},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Lanzinger, Matthias and Okulmus, Cem and Pichler, Reinhard and Selzer, Alexander and Gottlob, Georg},
year = {2025}
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 673 | Generalized Hypertree Decompositions: NP-Hardness and Tractable Variants | 2007 | PODS | 0.00015095061 |
| 2,726 | TPC-DS, Taking Decision Support Benchmarking to the Next Level | 2002 | SIGMOD | 8.203274e-05 |
| 3,070 | Accurate Summary-based Cardinality Estimation Through the Lens of Cardinality Estimation Graphs | 2022 | VLDB | 7.7900444e-05 |
| 3,622 | Robust Join Processing with Diamond Hardened Joins | 2024 | VLDB | 7.2465862e-05 |
| 5,461 | HyperBench: A Benchmark and Tool for Hypergraphs and Empirical Findings | 2019 | PODS | 6.2090515e-05 |
| 7,923 | Ranked Enumeration of Minimal Triangulations | 2019 | PODS | 5.5181056e-05 |
| 8,675 | Fast Parallel Hypertree Decompositions in Logarithmic Recursion Depth | 2022 | PODS | 5.3868551e-05 |
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