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)
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Authors
- 1. Daniel Kang (Stanford University)
- 2. John Guibas (Stanford University)
- 3. Peter D. Bailis (Stanford University)
- 4. Tatsunori Hashimoto (Stanford University)
- 5. Matei Zaharia (Stanford University)
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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