SemCEB is the first benchmark for cardinality estimation over semantic operators, evaluating sampling methods and Semantic Histograms on accuracy, cost, latency, and memory using 102 queries on a real-world dataset.
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cs.DB 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
Semantic Histograms treat semantic image filters as implicit range queries in embedding space and use two specificity estimators whose ensemble reduces end-to-end query optimization and execution overhead by up to 86%.
MLSkip demonstrates that lightweight metadata enables data skipping for ReLU-based ML filters, with 27.4% average pruning using min-max and 38.31% using 2D convex hulls on TPC benchmarks, for a 1.07x end-to-end speedup.
citing papers explorer
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SemCEB: A Cardinality Estimation Benchmark for Semantic Operators
SemCEB is the first benchmark for cardinality estimation over semantic operators, evaluating sampling methods and Semantic Histograms on accuracy, cost, latency, and memory using 102 queries on a real-world dataset.
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Selectivity Estimation for Semantic Filters on Image Data
Semantic Histograms treat semantic image filters as implicit range queries in embedding space and use two specificity estimators whose ensemble reduces end-to-end query optimization and execution overhead by up to 86%.
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MLSkip: Data Skipping for ML Filters via Lightweight Metadata
MLSkip demonstrates that lightweight metadata enables data skipping for ReLU-based ML filters, with 27.4% average pruning using min-max and 38.31% using 2D convex hulls on TPC benchmarks, for a 1.07x end-to-end speedup.