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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
6411
Venue
SIGMOD
Year
2022
Pagerank
7.1430942e-05
Overall Rank
3,766 | 74.17%
DOI
10.1145/3514221.3517897

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{kang_sigmod22,
        title = {{TASTI: Semantic Indexes for Machine Learning-based Queries over Unstructured Data}},
        author = {Kang, Daniel and Guibas, John and Bailis, Peter D. and Hashimoto, Tatsunori and Zaharia, Matei},
        series = {{SIGMOD} '22},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3514221.3517897},
        url = {https://dl.acm.org/doi/10.1145/3514221.3517897},
        year = {2022}
}

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