DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation
Summary: Targets declarative top-K “interpretation-by-example” queries over DNN activations via a compact indexing scheme and optimized execution. Instance-optimal algorithm + <20% materialization cost yields up to 63x single-query speedups and consistent multi-query dominance. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Dong He (University of Washington)
- 2. Maureen Daum (University of Washington)
- 3. Walter Cai (University of Washington)
- 4. Magdalena Balazinska (University of Washington)
BibTeX Citation
@article{he_vldb22,
title = {{DeepEverest: Accelerating Declarative Top-K Queries for Deep Neural Network Interpretation}},
author = {He, Dong and Daum, Maureen and Cai, Walter and Balazinska, Magdalena},
journal = {PVLDB},
series = {{VLDB} '22},
volume = {15},
number = {1},
pages = {98--111},
doi = {10.14778/3485450.3485460},
url = {https://doi.org/10.14778/3485450.3485460},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,362 | EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions | 2023 | VLDB | 5.4432545e-05 |
| 9,464 | Self-Enhancing Video Data Management System for Compositional Events with Large Language Models | 2025 | SIGMOD | 5.2634238e-05 |
| 11,219 | MetaStore: Analyzing Deep Learning Meta-Data at Scale | 2024 | VLDB | 5.093636e-05 |
| 11,305 | Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows | 2024 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 10 of 10 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.0020210012 |
| 5 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0010828372 |
| 1,475 | VisTrails: Visualization meets Data Management | 2006 | SIGMOD | 0.00010662942 |
| 1,519 | Top-k Query Evaluation with Probabilistic Guarantees | 2004 | VLDB | 0.00010513777 |
| 1,569 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.00010335423 |
| 1,670 | MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis | 2018 | SIGMOD | 0.00010045615 |
| 1,967 | IO-Top-k: Index-access Optimized Top-k Query Processing | 2006 | VLDB | 9.3804693e-05 |
| 3,017 | Joining Ranked Inputs in Practice | 2002 | VLDB | 7.8483041e-05 |
| 3,702 | Best Position Algorithms for Top-k Queries | 2007 | VLDB | 7.1838636e-05 |
| 6,427 | DeepBase: Deep Inspection of Neural Networks | 2019 | SIGMOD | 5.8808145e-05 |
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| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
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| 2 | 6,708 | Serving Deep Learning Models with Deduplication from Relational Databases | 2022 | VLDB |
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| 7 | 563 | Plan-Structured Deep Neural Network Models for Query Performance Prediction | 2019 | VLDB |
| 8 | 5,827 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD |
| 9 | 9,215 | Deep Query Optimization | 2019 | SIGMOD |
| 10 | 9,927 | Everest: A Top-K Deep Video Analytics System | 2022 | SIGMOD |