Accelerating Queries over Unstructured Data with ML
Summary: MEME accelerates queries over unstructured data by using cheap proxy ML models and indexes to approximate costly oracle extractors (DNNs/humans) and reduce labeling costs. Unlike prior proxy work, it provides statistical guarantees on results and enables cross-query work sharing. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Daniel Kang
BibTeX Citation
@inproceedings{kang_cidr21,
address = {Amsterdam, Netherlands},
series = {{CIDR} '21},
title = {{Accelerating Queries over Unstructured Data with ML}},
booktitle = {Proceedings of the {Conference} on {Innovative} {Data} {Systems} {Research}},
author = {Kang, Daniel},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 271 | NoScope: Optimizing Neural Network Queries over Video at Scale | 2017 | VLDB | 0.00022560564 |
| 281 | Accelerating Machine Learning Inference with Probabilistic Predicates | 2018 | SIGMOD | 0.00022295232 |
| 541 | BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics | 2020 | VLDB | 0.00016657685 |
| 2,776 | Approximate Selection with Guarantees using Proxies | 2020 | VLDB | 8.0309448e-05 |
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