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TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data

Summary: Proposes TASTI, a trainable semantic index replacing per-query proxies with embeddings so similar records share outputs. Theoretically ties embedding error to accuracy; empirically on five multimodal datasets, it builds 10x cheaper indexes and 24x faster proxy queries. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6349
Venue
SIGMOD
Year
2022
Pagerank
6.137686e-05
Overall Rank
4,501 | 68.69%
DOI
10.1145/3514221.3517897

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