Matthias Lanzinger
@mlanzinger.bsky.social
Assistant Professor TU Wien, previously Uni of Oxford | Research in #databasetheory, #AI & #GNNs
5/5
Finally, a prototype implementation demonstrates the practical impact on query evaluation. Early experiments show potential for large performance gains on difficult queries in standard database systems (e.g., PostgreSQL, SparkSQL)
Check it out: arxiv.org/pdf/2412.11669
Finally, a prototype implementation demonstrates the practical impact on query evaluation. Early experiments show potential for large performance gains on difficult queries in standard database systems (e.g., PostgreSQL, SparkSQL)
Check it out: arxiv.org/pdf/2412.11669
arxiv.org
December 20, 2024 at 8:40 AM
5/5
Finally, a prototype implementation demonstrates the practical impact on query evaluation. Early experiments show potential for large performance gains on difficult queries in standard database systems (e.g., PostgreSQL, SparkSQL)
Check it out: arxiv.org/pdf/2412.11669
Finally, a prototype implementation demonstrates the practical impact on query evaluation. Early experiments show potential for large performance gains on difficult queries in standard database systems (e.g., PostgreSQL, SparkSQL)
Check it out: arxiv.org/pdf/2412.11669
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.
3/ 🚀 Our contribution:
We propose Soft Hypertree Width (SHW) – a relaxed version of hypertree width that provides additional algorithmic flexibility, which allows for efficiently incorporating preferences and constraints into query decompositions.
We propose Soft Hypertree Width (SHW) – a relaxed version of hypertree width that provides additional algorithmic flexibility, which allows for efficiently incorporating preferences and constraints into query decompositions.
December 20, 2024 at 8:39 AM
3/ 🚀 Our contribution:
We propose Soft Hypertree Width (SHW) – a relaxed version of hypertree width that provides additional algorithmic flexibility, which allows for efficiently incorporating preferences and constraints into query decompositions.
We propose Soft Hypertree Width (SHW) – a relaxed version of hypertree width that provides additional algorithmic flexibility, which allows for efficiently incorporating preferences and constraints into query decompositions.
2/ The problem: Theoretical performance of queries in databases depends on the "width" of decompositions of the query. The smaller the “width”, the more efficient evaluation is. But real-world performance isn’t just about the smallest width… constraints & preferences matter too!
December 20, 2024 at 8:38 AM
2/ The problem: Theoretical performance of queries in databases depends on the "width" of decompositions of the query. The smaller the “width”, the more efficient evaluation is. But real-world performance isn’t just about the smallest width… constraints & preferences matter too!
Also more recently arxiv.org/abs/2410.18676 :)
Homomorphism Counts as Structural Encodings for Graph Learning
Graph Transformers are popular neural networks that extend the well-known Transformer architecture to the graph domain. These architectures operate by applying self-attention on graph nodes and incorp...
arxiv.org
November 25, 2024 at 2:18 PM
Also more recently arxiv.org/abs/2410.18676 :)