Seiden: Revisiting Query Processing in Video Database Systems
Summary: Notes modern oracle CV models rival or exceed proxy latency, so Seiden builds a query-agnostic index by running the oracle on a subset of frames. At query time it uses exploration–exploitation sampling and temporal continuity to answer queries faster and more accurately than SoTA (≈6.6× speedup). (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jaeho Bang
- 2. Gaurav Tarlok Kakkar
- 3. Pramod Chunduri
- 4. Subrata Mitra
- 5. Joy Arulraj
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,334 | Aero: Adaptive Query Processing of ML Queries | 2025 | SIGMOD | 4.7538944e-05 |
| 9,373 | Falcon: Fair Active Learning using Multi-armed Bandits | 2024 | VLDB | 4.3460825e-05 |
| 10,337 | KEN: An Execution Engine for Unstructured Database Systems | 2026 | VLDB | 4.1905499e-05 |
| 10,394 | MAST: Towards Efficient Analytical Query Processing on Point Cloud Data | 2025 | SIGMOD | 4.1905499e-05 |
| 10,512 | Self-Enhancing Video Data Management System for Compositional Events with Large Language Models | 2025 | SIGMOD | 4.1905499e-05 |
| 10,532 | Scalable Complex Event Processing on Video Streams | 2025 | SIGMOD | 4.1905499e-05 |
| 10,675 | Déjà Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse | 2025 | VLDB | 4.1905499e-05 |
| 11,064 | Optimizing Video Queries with Declarative Clues | 2024 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,429 | Accelerating Queries over Unstructured Data with ML | 2021 | CIDR | 4.1905499e-05 |
| 461 | SeeDB: Efficient Data-Driven Visualization Recommendations to Support Visual Analytics | 2015 | VLDB | 0.00022615628 |
| 8,646 | Optimizing Video Selection LIMIT Queries With Commonsense Knowledge | 2024 | VLDB | 4.4720866e-05 |
| 9,770 | DoveDB: A Declarative and Low-Latency Video Database | 2023 | VLDB | 4.2815042e-05 |
| 9,346 | SketchQL: Video Moment Querying with a Visual Query Interface | 2024 | SIGMOD | 4.3512358e-05 |
| 14,136 | Modelling and Querying Video Data | 1994 | VLDB | - |
| 9,767 | TVM: A Tile-based Video Management Framework | 2024 | VLDB | 4.2815042e-05 |
| 5,263 | SeeDB: Visualizing Database Queries Efficiently | 2014 | VLDB | 5.5959099e-05 |
| 5,038 | VisualWorldDB: A DBMS for the Visual World | 2020 | CIDR | 5.7376543e-05 |
| 11,064 | Optimizing Video Queries with Declarative Clues | 2024 | VLDB | 4.1905499e-05 |