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Sia: Optimizing Queries using Learned Predicates

Summary: counter-examples and SMT-based verification to learn predicates with guaranteed semantics and controllable column sets, enabling broader predicate-centric rewrites. On 200 TPC-H queries, SIA rewrites 114 and yields >2x speedups in 66 cases. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6153
Venue
SIGMOD
Year
2021
Pagerank
4.7764688e-05
Overall Rank
7,283 | 49.34%
DOI
10.1145/3448016.3457262

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Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
7,753 Rethinking Learned Cost Models: Why Start from Scratch? 2023 SIGMOD 4.660151e-05
7,854 dbET: Execution Time Distribution-based Plan Selection 2023 SIGMOD 4.6350172e-05
8,345 SlabCity: Whole-Query Optimization using Program Synthesis 2023 VLDB 4.5426916e-05
8,645 Predicate Pushdown for Data Science Pipelines 2023 SIGMOD 4.4772518e-05
10,950 PLAQUE: Automated Predicate Learning at Query Time 2024 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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