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
- 2. Maureen Daum
- 3. Walter Cai
- 4. Magdalena Balazinska
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,382 | EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions | 2023 | VLDB | 4.5263687e-05 |
| 10,512 | Self-Enhancing Video Data Management System for Compositional Events with Large Language Models | 2025 | SIGMOD | 4.1905499e-05 |
| 11,011 | MetaStore: Analyzing Deep Learning Meta-Data at Scale | 2024 | VLDB | 4.1905499e-05 |
| 11,102 | Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows | 2024 | VLDB | 4.1905499e-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 | 0.0032118946 |
| 8 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0015436578 |
| 1,412 | VisTrails: Visualization meets Data Management | 2006 | SIGMOD | 0.00012112718 |
| 1,666 | HELIX: Holistic Optimization for Accelerating Iterative Machine Learning | 2019 | VLDB | 0.00010955907 |
| 1,805 | Top-k Query Evaluation with Probabilistic Guarantees | 2004 | VLDB | 0.00010479371 |
| 2,014 | IO-Top-k: Index-access Optimized Top-k Query Processing | 2006 | VLDB | 9.7982231e-05 |
| 2,157 | MISTIQUE: A System to Store and Query Model Intermediates for Model Diagnosis | 2018 | SIGMOD | 9.4153917e-05 |
| 2,891 | Joining Ranked Inputs in Practice | 2002 | VLDB | 7.9577748e-05 |
| 4,178 | Best Position Algorithms for Top-k Queries | 2007 | VLDB | 6.3757762e-05 |
| 6,371 | DeepBase: Deep Inspection of Neural Networks | 2019 | SIGMOD | 5.0880545e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,055 | Serving Deep Learning Models with Deduplication from Relational Databases | 2022 | VLDB | 4.8428976e-05 |
| 11,352 | DeepO: A Learned Query Optimizer | 2022 | SIGMOD | 4.1905499e-05 |
| 5,524 | Facilitating SQL Query Composition and Analysis | 2020 | SIGMOD | 5.4589341e-05 |
| 5,799 | Learned Approximate Query Processing: Make it Light, Accurate and Fast | 2021 | CIDR | 5.3219666e-05 |
| 3,658 | Towards a Hands-Free Query Optimizer through Deep Learning | 2019 | CIDR | 6.8700949e-05 |
| 13,242 | Using Deep Learning Models to Replace Large Materialized Views in Relational Database | 2021 | CIDR | - |
| 876 | Plan-Structured Deep Neural Network Models for Query Performance Prediction | 2019 | VLDB | 0.00015660534 |
| 6,184 | Top-K Deep Video Analytics: A Probabilistic Approach | 2021 | SIGMOD | 5.1636368e-05 |
| 9,117 | Deep Query Optimization | 2019 | SIGMOD | 4.3885415e-05 |
| 9,772 | Everest: A Top-K Deep Video Analytics System | 2022 | SIGMOD | 4.2815042e-05 |