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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)

Paper ID
399
Venue
CIDR
Year
2021
Pagerank
4.1945683e-05
Overall Rank
11,426 | 20.52%
DOI
10.1145/nnnnnnn.nnnnnnn

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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
316 NoScope: Optimizing Neural Network Queries over Video at Scale 2017 VLDB 0.00027988668
329 Accelerating Machine Learning Inference with Probabilistic Predicates 2018 SIGMOD 0.00027249545
696 BlazeIt: Optimizing Declarative Aggregation and Limit Queries for Neural Network-Based Video Analytics 2020 VLDB 0.00018048935
3,558 Approximate Selection with Guarantees using Proxies 2020 VLDB 6.9765724e-05
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