Public articles linked to the same research event.
arXiv JEVDB is a scalable semantic database system that uses fast, typed decision models for semantic filters, joins, classification, and ranking while selectively escalating uncertain cases to generative LLMs; it combines Yannakakis-style semijoin reduction with Semantic Bloom Filters (SBFs) to cut semantic-join work, achieving the lowest latency on all 21 evaluated SemBench queries and the lowest cost on 19 with competitive answer quality, and on the TPC-DS-derived Shelob workload with joins scaling to 540K candidate pairs it completes all queries with 95.7%-97.5% mean F1, where SBF screening removes 87.4% of candidate pairs before semantic evaluation and reusable condition-index scoring further reduces reasoning-model escalations by 55.2%.
JEVDB is a scalable semantic database system that uses fast, typed decision models for semantic filters, joins, classification, and ranking while selectively escalating uncertain cases to generative LLMs; it combines Yannakakis-style semijoin reduction with Semantic Bloom Filters (SBFs) to cut semantic-join work, achieving the lowest latency on all 21 evaluated SemBench queries and the lowest cost on 19 with competitive answer quality, and on the TPC-DS-derived Shelob workload with joins scaling to 540K candidate pairs it completes all queries with 95.7%-97.5% mean F1, where SBF screening removes 87.4% of candidate pairs before semantic evaluation and reusable condition-index scoring further reduces reasoning-model escalations by 55.2%.
JEVDB is a scalable semantic database system that uses fast, typed decision models for semantic filters, joins, classification, and ranking while selectively escalating uncertain cases to generative LLMs; it combines Yannakakis-style semijoin reduction with Semantic Bloom Filters (SBFs) to cut semantic-join work, achieving the lowest latency on all 21 evaluated SemBench queries and the lowest cost on 19 with competitive answer quality, and on the TPC-DS-derived Shelob workload with joins scaling to 540K candidate pairs it completes all queries with 95.7%-97.5% mean F1, where SBF screening removes 87.4% of candidate pairs before semantic evaluation and reusable condition-index scoring further reduces reasoning-model escalations by 55.2%.
JEVDB is a scalable semantic database system that uses fast, typed decision models for semantic filters, joins, classification, and ranking while selectively escalating uncertain cases to generative LLMs; it combines Yannakakis-style semijoin reduction with Semantic Bloom Filters (SBFs) to cut semantic-join work, achieving the lowest latency on all 21 evaluated SemBench queries and the lowest cost on 19 with competitive answer quality, and on the TPC-DS-derived Shelob workload with joins scaling to 540K candidate pairs it completes all queries with 95.7%-97.5% mean F1, where SBF screening removes 87.4% of candidate pairs before semantic evaluation and reusable condition-index scoring further reduces reasoning-model escalations by 55.2%.