Matthias Lanzinger
@mlanzinger.bsky.social
Assistant Professor TU Wien, previously Uni of Oxford | Research in #databasetheory, #AI & #GNNs
4/
• SHW can achieve smaller widths than HW while remaining tractable to compute, boosting efficiency.
• SHW induces a hierarchy of width measures that equal generalised hypertree width in the limit.
• SHW can achieve smaller widths than HW while remaining tractable to compute, boosting efficiency.
• SHW induces a hierarchy of width measures that equal generalised hypertree width in the limit.
December 20, 2024 at 8:39 AM
4/
• SHW can achieve smaller widths than HW while remaining tractable to compute, boosting efficiency.
• SHW induces a hierarchy of width measures that equal generalised hypertree width in the limit.
• SHW can achieve smaller widths than HW while remaining tractable to compute, boosting efficiency.
• SHW induces a hierarchy of width measures that equal generalised hypertree width in the limit.
1/ 📢 Excited to share our new paper: “Soft and Constrained Hypertree Width” – a step forward in optimizing database queries by introducing a flexible, and effective hypergraph decomposition framework. 🧵 arxiv.org/pdf/2412.11669
December 20, 2024 at 8:38 AM
1/ 📢 Excited to share our new paper: “Soft and Constrained Hypertree Width” – a step forward in optimizing database queries by introducing a flexible, and effective hypergraph decomposition framework. 🧵 arxiv.org/pdf/2412.11669