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KEN: An Execution Engine for Unstructured Database Systems
Summary: KEN: execution engine for UDBMSs that makes model cascades practical by dynamically adapting cascade use to workload and optimizing GPU placement/scheduling. Key novelty: exposes middle-ground accuracy/latency tradeoffs for unstructured operators, avoiding the usual small-vs-LMM binary.
(summarized by gpt-5.4-mini on Apr 12 2026)
- Paper ID
- 14382
- Venue
- VLDB
- Year
- 2026
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,337 | 28.16%
- DOI
-
10.14778/3796195.3796204
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Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
Outgoing Citations (Sorted by Pagerank)
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2017 |
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0.0002798145 |
| 332 |
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0.00027173479 |
| 1,390 |
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2020 |
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0.00012242018 |
| 1,839 |
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2025 |
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2020 |
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EVA: A Symbolic Approach to Accelerating Exploratory Video Analytics with Materialized Views |
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6.919859e-05 |
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Vaas: Video Analytics At Scale |
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6.3414314e-05 |
| 4,565 |
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6.0746821e-05 |
| 4,686 |
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2023 |
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5.9929067e-05 |
| 4,745 |
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2019 |
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5.9466323e-05 |
| 4,865 |
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2022 |
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5.8627966e-05 |
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2024 |
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5.3060581e-05 |
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2021 |
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