Publikation
Selectivity Estimation for Semantic Filters on Image Data
Matthias Urban; Vu Huy Nguyen; Gabriele Sanmartino; Paolo Papotti; Carsten Binnig
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.04610, Pages 1-8, arXiv, 2026.
Zusammenfassung
Semantic data systems integrate Large Language Models (LLMs)
and Vision-Language Models (VLMs) directly into database query
execution, enabling expressive queries on multi-modal data. How-
ever, optimizing these queries requires accurate selectivity esti-
mates to determine the most efficient operator execution order.
Contemporary systems rely on online sample-based profiling, a
process that incurs severe latency overheads and struggles with low-
selectivity queries. In this paper, we introduce Semantic Histograms,
a novel selectivity estimator for semantic filters on image data that
leverages shared embedding spaces to bypass traditional profiling.
We realize that all semantic filters are implicit range queries, as
they match a range of different images. Some filter predicates are
more general, yielding a wide range, while others are more specific,
yielding a smaller range. To address the challenge of implicit ranges,
we propose two approaches to estimate the queries’ specificity, with
an ensemble of the two performing best. The evaluation shows that
Semantic Histograms can reduce the end-to-end runtime overhead
of query optimization and execution by up to 86%.