Proves that conditional residual answer entropy sets the query-time scale under a routed atom-budgeted certified-repair learned-index architecture.
VLDB Endow.17, 4 (2023), 849–862
3 Pith papers cite this work, alongside 6 external citations. Polarity classification is still indexing.
3
Pith papers citing it
6
external citations · external index
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
background 1representative citing papers
TEmBed benchmark shows that the best tabular embedding model depends on the specific task and the representation level (cell, row, column, or table).
Relational engines achieve faster SQL+vector-search queries on GPU than CPU when using compact vector indexes and fast interconnects, reversing the CPU-only design in current systems.
citing papers explorer
-
Residual-Entropy Accounting for Routed Atom-Budgeted Learned Indexes
Proves that conditional residual answer entropy sets the query-time scale under a routed atom-budgeted certified-repair learned-index architecture.
-
Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
TEmBed benchmark shows that the best tabular embedding model depends on the specific task and the representation level (cell, row, column, or table).
-
To GPU or Not to GPU: Vector Search in Relational Engines
Relational engines achieve faster SQL+vector-search queries on GPU than CPU when using compact vector indexes and fast interconnects, reversing the CPU-only design in current systems.